About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pre-training team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities: - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications: - Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and experience with deep learning frameworks (PyTorch preferred) - Familiarity with large-scale machine learning, particularly in the context of language models - Ability to balance research goals with practical engineering constraints - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment - Care about the societal impacts of your work Preferred Experience: - Work on high-performance, large-scale ML systems - Familiarity with GPUs, Kubernetes, and OS internals - Experience with language modeling using transformer architectures - Knowledge of reinforcement learning techniques - Background in large-scale ETL processes You'll thrive in this role if you: - Have significant software engineering experience - Are results-oriented with a bias towards flexibility and impact - Willingly take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work - Are eager to learn more about machine learning research - Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects - Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research - View research and engineering as

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality. Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation. If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you! You will: - Build, profile and optimize our training and inference framework - Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation - Research and integrate state-of-the-art technologies to optimize our ML system Ideally you’d have: - Strong excitement about system optimization - Experience with multi-node LLM training and inference - Experience with developing large-scale distributed ML systems - Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. - Strong written and verbal communication skills and the ability to operate in a cross functional team environment Nice to haves: - Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $189,600 - $237,000 USD PLEASE NOTE: Our po

ABOUT THE TEAM The Alignment Science team at OpenAI studies the science of intent alignment: how to train models to understand what users are actually asking for, act faithfully on that intent while respecting safety constraints, verify what they did, and report their limitations honestly. Our work sits alongside broader value alignment efforts, but this team focuses on scalable methods for ensuring instruction-following, honesty, and robustness as models become more capable. We work on both sides of alignment research: producing externally publishable results and integrating promising techniques into the models OpenAI deploys. Recent team research on model confessions studies how models can be trained to honestly report shortcomings after their original answer, including failures involving hallucination, instruction following, scheming, and reward hacking. That work reflects a broader agenda: build scalable and general methods to ensure models follow human intent. The team uses a mix of training and evaluation methods, with a focus on reinforcement learning. We care about rigorous, quantitative research that can translate into safer model behavior. ABOUT THE ROLE As a Research Engineer / Research Scientist on the Alignment team, you will design and run experiments that help increasingly capable models follow user intent, remain calibrated about correctness and risk, and honestly surface their own mistakes. You will work on hands-on model training, evaluation design, and research infrastructure, while helping turn promising alignment methods into techniques that can be used in frontier model development. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. We are also open to exceptional remote candidates who can operate independently and collaborate closely with the team. IN THIS ROLE, YOU WILL: - Design and implement alignment experiments focused on intent following, honesty, calibration, and robustness. - Train and evaluate models using reinforcement learning, and other empirical ML methods. - Develop evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming. - Study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives. - Build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems. - Investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure. - Integrate successful techniques into model training and deployment workflows. - Produce externally publishable research when results advance the broader science of alignment. - Collaborate with researchers and engineers across post-training, RL, evaluations, safety, and product-facing teams. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs. - Have excellent engineering skills in Python and modern ML frameworks such as PyTorch. - Bring mathematical rigor, quantitative taste, and comfort turning ambiguous research questions into measurable experiments. - Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research. - Can operate with high independence and do not need close day-to-day handholding. - Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change. - Have a strong record in technical problem solving, such as competitive programming, math contests, systems work, or similarly rigorous engineering and research projects. - Care about building AI systems that are trustworthy, honest, and reliable in high-stakes settings. - Are motivated by making concrete

ABOUT THE TEAM The Codex Research team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a member of the Codex Research team, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT: - Design and run experiments that improve agentic model behavior across coding, tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex, API/platform, ChatGPT, and general-agent product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals,

About the Team Our Safety Systems https://openai.com/safety/safety-systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency. The Model Policy team aligns model behavior with desired human values and norms. We co-design policy with models and for models by driving rapid policy taxonomy iteration based on data and defining evaluation criteria for foundational models’ ability to reason about safety. Key focus areas include: catastrophic risk, mental health, teen safety and multimodal safety. About the Role Providing access to frontier AI systems raises complex questions around dual-use science and catastrophic risk. How should models respond to requests involving chemical synthesis, biological experimentation, or pathogen research? Where is the boundary between legitimate scientific inquiry and information that could enable misuse? How do we design policies that meaningfully reduce risk without unnecessarily restricting beneficial research? This is a senior role in which you’ll help shape policy creation and development at OpenAI for addressing biological and chemical risks. You will develop structured policy frameworks and taxonomies to guide safe model behavior. This role sits at the intersection of biosecurity expertise, AI safety research, and policy design. You will help ensure that frontier AI systems can support beneficial life sciences research, such as drug discovery, public health, and biosafety, while reducing the risk that these capabilities could be misused. Our relevant publications: - Preparedness framework https://openai.com/index/updating-our-preparedness-framework/ - Preparing for future AI capabilities in biology https://openai.com/index/preparing-for-future-ai-capabilities-in-biology/ - Safety evaluations hub https://openai.com/safety/evaluations-hub/ - OpenAI GPT5 System Card https://openai.com/index/gpt-5-system-card/ - Evaluating Fairness in ChatGPT https://openai.com/index/evaluating-fairness-in-chatgpt/ - Improving Model Safety Behavior with Rule-Based Rewards https://openai.com/index/improving-model-safety-behavior-with-rule-based-rewards/ - OpenAI Model Spec https://openai.com/index/introducing-the-model-spec/ Your Responsibilities: - Design and maintain model policies governing chemical and biological risk, defining how models should safely handle dual-use scenarios. - Develop structured taxonomies of chemical and biological risk that inform model training data, evaluation benchmarks, and safety monitoring systems. - Translate biosecurity and chemical security expertise into actionable model behavior, working closely with research and engineering teams to operationalize policy in training and evaluation pipelines. - Develop a broad range of subject matter expertise while maintaining agility across topics. - Identify emerging risk vectors where frontier AI capabilities could meaningfully lower barriers to harmful activity and develop mitigation strategies. - Engage with internal and external subject-matter experts in biosecurity, biodefense, and chemical safety to ensure policies reflect real-world risk landscapes. You might thrive in this role if you: - Have strong domain expertise in chemistry, biology, biosecurity, or related fields and are motivated to translate that expertise into principled, operational policies that scale to frontier AI systems. - Have experience researching or working with LLMs, machine learning, AI governance, technology policy, or related areas, and enjoy tackling structured reasoning and classification problems—such as defining boundaries between legitimate scientific inquiry and potentially harmful applications. - Have experience designing, refining, or enforcing policies or safeguards for complex systems, whether in AI/ML environments, scientific research governance, national security contexts, or other high-stakes technical domains. - Are comfortable navigating a

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: - Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability - Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure - Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance - Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams - Build and maintain production logging, monitoring dashboards, and evaluation infrastructure - Add new capabilities to the training codebase, such as long context support or novel architectures - Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams - Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned You May Be a Good Fit If You: - Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems - Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other - Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure - Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs - Excel at debugging complex, ambiguous problems across multiple layers of the stack - Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents - Are passionate about the work itself and want to refine your craft as a research engineer - Care about the societal impacts of AI and responsible scaling Strong Candidates May Also Have: - Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale - Contributed to open-source LLM frame

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Note: we don't have open Research Manager positions on the Interpretability team at this time. However, we're actively growing our team of Research Engineers and Research Scientists . If you're excited about interpretability research and open to an individual contributor role, we encourage you to apply. About the Interpretability team When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team’s mission is to reverse engineer how trained models work, and Interpretability research is one of Anthropic’s core research bets on AI safety. We believe that a mechanistic understanding is the most robust way to make advanced systems safe. People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks, or as treating neural networks as binary computer programs we're trying to "reverse engineer". We aim to create a solid scientific foundation for mechanistically understanding neural networks and making them safe (see our vision post ). We have focused on resolving the issue of "superposition" (see Toy Models of Superposition , Superposition, Memorization, and Double Descent , and our May 2023 update ), which causes the computational units of the models, like neurons and attention heads, to be individually uninterpretable, and on finding ways to decompose models into more interpretable components. Our subsequent work which found millions of features in Claude 3.0 Sonnet, one of our production language models, represents progress in this direction. In our most recent work , we developed methods that allow us to build circuits using features and use these circuits to understand the mechanisms associated with a model's computation and study specific examples of multi-hop reasoning, planning, and chain-of-thought faithfulness on Claude Haiku 3.5, one of our production models.” This is a stepping stone towards our overall goal of mechanistically understanding neural networks. A few places to learn more about our work and team are this introduction to Interpretability from our research lead, Chris Olah, Stanford CS25 lecture given by Josh Batson, and TWIML AI podcast with E

About the Team The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale. About the Role As a Research Engineer/Research Scientist at OpenAI, you will advance the frontier of AI alignment and capabilities through cutting-edge RL methods. Your work will sit at the heart of training intelligent, aligned, and general-purpose agents, including the systems that power various models. We’re looking for people who have a background in reinforcement learning research, are able to iterate quickly, and are proficient at coding. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if: - You love being on the cutting edge of RL and language model research. - You’re a self-starter who takes initiative and ownership of ideas, driving them to completion. - You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time. - You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key. - You’re comfortable diving into a large ML codebase to debug and improve it. - You have a deep understanding of machine learning and machine learning applications. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241. OpenAI Global

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: - Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability - Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure - Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance - Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams - Build and maintain production logging, monitoring dashboards, and evaluation infrastructure - Add new capabilities to the training codebase, such as long context support or novel architectures - Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams - Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned You May Be a Good Fit If You: - Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems - Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other - Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure - Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs - Excel at debugging complex, ambiguous problems across multiple layers of the stack - Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents - Are passionate about the work itself and want to refine your craft as a research engineer - Care about the societal impacts of AI and responsible scaling Strong Candidates May Also Have: - Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale - Contributed to open-source LLM frame

About the Team The Integrity team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary, but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale. The Integrity team is at the front lines of defending against misuse in all its forms: content abuse, scaled attacks, and other actions that could undermine the user experience or harm our operational stability. About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will have the opportunity to work with some of the brightest minds in AI. You’ll work on state-of-the-art models and classifiers, experiment with new architecture and approaches, and push forward our abilities in content and user understanding. You’ll help turn research breakthroughs into tangible solutions that improve the trust and safety of our platform. If you're excited about training LLMs and building ML models, this role is your chance to make a significant mark. In this role, you will: - Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact. - Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives. - Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches. - Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices. - Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large. You might thrive in this role if you: - Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field. - Demonstrated experience in deep learning and transformers models - Experience with content understanding or abuse prevention with LLMs is a plus - Proficiency in frameworks like PyTorch or Tensorflow - Strong foundation in data structures, algorithms, and software engineering principles. - Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization - Excellent problem-solving and analytical skills, with a proactive approach to challenges. - Ability to work collaboratively with cross-functional teams. - Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines - Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employm

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software generation, improving reasoning abilities in areas such as mathematics, and developing prototypes for internal use, productivity, and evaluation. Representative projects: - Architect and optimize core reinforcement learning infrastructure, from clean training abstractions to distributed experiment management across GPU clusters. Help scale our systems to handle increasingly complex research workflows. - Design, implement, and test novel training environments, evaluations, and methodologies for reinforcement learning agents which push the state of the art for the next generation of models. - Drive performance improvements across our stack through profiling, optimization, and benchmarking. Implement efficient caching solutions and debug distributed systems to accelerate both training and evaluation workflows. - Collaborate across research and engineering teams to develop automated testing frameworks, design clean APIs, and build scalable infrastructure that accelerates AI research. You may be a good fit if you: - Are proficient in Python and async/concurrent programming with frameworks like Trio - Have experience with machine learning frameworks (PyTorch, TensorFlow, JAX) - Have industry experience in machine learning research - Can balance research exploration with engineering implementation&lt

ABOUT THE TEAM Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity. Our work involves: 1. Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. 2. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. 3. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework https://openai.com/index/updating-our-preparedness-framework/, and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. ABOUT THE ROLE Models are becoming increasingly capable—moving from tools that assist humans to agents that can plan, execute, and adapt in the real world. As we push toward AGI, cybersecurity becomes one of the most important and urgent frontiers: the same systems that can accelerate productivity can also accelerate exploitation. As a Researcher for cybersecurity risks, you will help design and implement an end-to-end mitigation stack to reduce severe cyber misuse across OpenAI’s products. This role requires strong technical depth and close cross-functional collaboration to ensure safeguards are enforceable, scalable, and effective. You’ll contribute directly to building protections that remain robust as products, model capabilities, and attacker behaviors evolve. IN THIS ROLE, YOU WILL: - Design and implement mitigation components for model-enabled cybersecurity misuse—spanning prevention, monitoring, detection, and enforcement—under the guidance of senior technical and risk leadership. - Integrate safeguards across product surfaces in partnership with product and engineering teams, helping ensure protections are consistent, low-latency, and scale with usage and new model capabilities. - Evaluate technical trade-offs within the cybersecurity risk domain (coverage, latency, model utility, and user privacy) and propose pragmatic, testable solutions. - Collaborate closely with risk and threat modeling partners to align mitigation design with anticipated attacker behaviors and high-impact misuse scenarios. - Execute rigorous testing and red-teaming workflows, helping stress-test the mitigation stack against evolving threats (e.g., novel exploits, tool-use chains, automated attack workflows) and across different product surfaces—then iterate based on findings. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have a passion for AI safety and are motivated to make cutting-edge AI models safer for real-world use. - Bring demonstrated experience in deep learning and transformer models. - Are proficient with frameworks such as PyTorch or TensorFlow. - Possess a strong foundation in data structures, algorithms, and software engineering principles. - Are familiar with methods for training and fine-tuning large language models, including distillation, supervised fine-tuning, and policy optimization. - Excel at working collaboratively with cross-functional teams across research, security, policy, product, and engineering. - Have significant experience designing and deploying technical safeguards for abuse prevention, detection, and enforcement at scale. - (Nice to have) Bring background knowledge in cybersecurity or adjacent fields. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectr

About the Team OpenAI’s Hardware organization develops silicon and system-level solutions designed for the unique demands of advanced AI workloads. The team is responsible for building the next generation of AI-native silicon while working closely with software and research partners to co-design hardware tightly integrated with AI models. In addition to delivering production-grade silicon for OpenAI’s supercomputing infrastructure, the team also creates custom design tools and methodologies that accelerate innovation and enable hardware optimized specifically for AI. About the Role As an Engineer on our hardware optimization and co-design team, you will co-design future hardware from different vendors for programmability and performance. You will work with our kernel, compiler and machine learning engineers to understand their unique needs related to ML techniques, algorithms, numerical approximations, programming expressivity, and compiler optimizations. You will evangelize these constraints with various vendors to develop and influence future hardware architectures towards efficient training and inference on our models. If you are excited about efficiently distributing a large language model across devices, dealing with and optimizing system-wide/rack-wide networking bottlenecks and eventually tailoring the compute pipe and memory hierarchy of the hardware platform, simulating workloads at different abstractions and working closely with our partners, this is the perfect opportunity! This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. Key Responsibilities - Co-design future hardware for programmability and performance with our hardware vendors - Assist hardware vendors in developing optimal kernels and add support for it in our compiler - Develop performance estimates for critical kernels for different hardware configurations and drive decisions on compute core and memory hierarchy features - Build system performance models at different abstraction levels and carry out analysis to drive decisions on scale up, scale out, front end networking - Work with machine learning engineers, kernel engineers and compiler developers to understand their vision and needs from high performance accelerators - Manage communication and coordination with internal and external partners - Influence the roadmap of hardware partners to optimize them for OpenAI’s workloads. - Evaluate potential partners’ accelerators and platforms. - As the scope of the role and team grows, understand and influence roadmaps for hardware partners for our datacenter networks, racks, and buildings. Qualifications - 4+ years of industry experience, including experience harnessing compute at scale and optimizing ML platform code to run efficiently on target hardware. - Strong experience in software/hardware co-design - Deep understanding of GPU and/or other AI accelerators - Experience with CUDA, Triton or a related accelerator programming language - Experience driving Machine Learning accuracy with low precision formats - Experience with system performance modeling and analysis to optimize ML model deployment - Strong coding skills in C/C++ and Python - Are familiar with the fundamentals of deep learning computing and chip architecture/microarchitecture. - Able to actively collaborate with ML engineers, kernel writers, compiler developers, system engineers, chip architects/microarchitects Preferred Skills - PhD in Computer Science and Engineering with a specialization in Computer Architecture, Parallel Computing. Compilers or other Systems - Strong understanding of LLMs and challenges related to their training and inference About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pretraining team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities: - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications: - Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and experience with deep learning frameworks (PyTorch preferred) - Familiarity with large-scale machine learning, particularly in the context of language models - Ability to balance research goals with practical engineering constraints - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment - Care about the societal impacts of your work Preferred Experience: - Work on high-performance, large-scale ML systems - Familiarity with GPUs, Kubernetes, and OS internals - Experience with language modeling using transformer architectures - Knowledge of reinforcement learning techniques - Background in large-scale ETL processes You'll thrive in this role if you: - Have significant software engineering experience - Are results-oriented with a bias towards flexibility and impact - Willingly take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work &l

About Scale AI Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. Role Overview As a Frontier Agents Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments. This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows. Key Responsibilities Customer Integration & Deployment - Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements - Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs) - Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows - Deploy and configure AI models and agents within customer security and compliance boundaries AI Agent Development - Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation - Architect multi-agent systems that orchestrate between different models, tools, and data sources - Implement evaluation frameworks to measure agent performance and iterate toward business objectives - Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement Prompt Engineering & Optimization - Create sophisticated prompt engineering strategies optimized for customer-specific domains and data - Build and maintain prompt libraries, templates, and best practices for customer use cases - Conduct systematic prompt experimentation and A/B testing to improve model outputs - Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate Technical Leadership & Collaboration - Serve as the primary technical point of contact for strategic enterprise accounts - Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration - Provide technical training and knowledge transfer to customer teams - Work closely with Scale's product and engineering teams to translate customer needs into product improvements - Document technical architectures, integration patterns, and best practices Problem Solving & Innovation - Debug complex technical issues across the entire stack, from data pipelines to model outputs - Rapidly prototype solutions to unblock customers and prove out new use cases - St

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team We are seeking passionate Research Scientists and Engineers to join our growing Pre-training team in Zurich. We are involved in developing the next generation of large language models. The team primarily focuses on multimodal capabilities: giving LLMs the ability to understand and interact with modalities other than text. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Responsibilities In this role you will interact with many parts of the engineering and research stacks. - Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development - Independently lead small research projects while collaborating with team members on larger initiatives - Design, run, and analyze scientific experiments to advance our understanding of large language models - Optimize and scale our training infrastructure to improve efficiency and reliability - Develop and improve dev tooling to enhance team productivity - Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications & Experience We encourage you to apply even if you do not believe you meet every single criterion. Because we focus on so many areas, the team is looking for both experienced engineers and strong researchers, and encourage anyone along the researcher/engineer spectrum to apply. - Degree (BA required, MS or PhD preferred) in Computer Science, Machine Learning, or a related field - Strong software engineering skills with a proven track record of building complex systems - Expertise in Python and deep learning frameworks - Have worked on high-performance, large-scale ML systems, particularly in the context of language modeling - Familiarity with ML Accelerators, Kubernetes, and large-scale data processing - Strong problem-solving skills and a results-oriented mindset - Excellent communication skills and ability to work in a collaborative environment You'll thrive in this role if you - Have significant software engineering experience - Are able to balance research goals with practical engineering constraints - Are happy to take on tasks outside your job description to support the team - Enjoy pair programming and collaborative work - Are eager to learn more about machine learning research &l

Scale Labs, Research Scientist — Safety Post Training As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer, and better understood by researchers and policymakers.. For example, you might: - Design and run post-training pipelines to study how training choices affect model safety, robustness, and alignment properties; - Develop interpretability-informed evaluations that reveal how and why models produce unsafe, deceptive, or otherwise undesirable behaviors, and use those insights to guide targeted mitigations; - Collaborate with policymakers, engineers, and other researchers to translate post-training and interpretability findings into actionable safety standards, evaluation benchmarks, and best practices. Ideally you’d have: - Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. - Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches. - A track record of published research in machine learning, particularly in generative AI. - At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development. - Strong written and verbal communication skills to operate in a cross-functional team. Nice to have: - Experience with mechanistic interpretability, probing, or other techniques for understanding model internals. - Familiarity with red-teaming or adversarial evaluation of post-trained models. - Experience studying failure modes introduced or masked by post-training, such as reward hacking, sycophancy, or alignment faking. Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional facto

ABOUT THE TEAM Integrity Data Science sits at the center of OpenAI’s mission to deploy powerful AI responsibly. We help ensure people can trust our products by building measurement systems, experimentation practices, and detection/mitigation strategies that protect OpenAI and our users from misuse, fraud, and evolving adversarial behaviors. As the scope and urgency of Integrity work expands across product surfaces and go-to-market motion, we’re hiring a dedicated Data Science Manager to scale the team, strengthen execution across multiple Integrity domains, and deepen partnership with Product, Engineering, Operations, and adjacent orgs (e.g., Growth, Ads). This role is based in our San Francisco HQ (in-office). ABOUT THE ROLE As Data Science Manager, Integrity, you will lead a team of data scientists working across trust & safety, fraud prevention, risk analysis, measurement, and modeling. You’ll be accountable for building a high-performing DS function that can keep pace with fast-moving threats—and for shaping the analytical strategy that informs how OpenAI detects, measures, and mitigates integrity risks at scale. This is a highly cross-functional leadership role. You’ll help set the roadmap with Integrity Product/Engineering leaders, evolve team structure and operating rhythms, raise the bar on technical rigor (experimentation, causal inference, modeling, metrics), and develop a culture of proactive, high-leverage impact. Many of the challenges in this space are emergent—new misuse patterns appear as the technology and ecosystem evolves—so this role requires strong judgment, comfort with ambiguity, and an ability to build systems that scale. IN THIS ROLE, YOU WILL: - Lead and scale a high-impact Integrity Data Science team—hiring, coaching, and developing DS ICs (and potentially future managers) while setting a strong technical and cultural bar. - Drive strategy across multiple Integrity domains (policy enforcement, bot detection, fraud prevention, IP theft, risk measurement, abuse prevention), balancing near-term response with durable systems. - Build and institutionalize analytical rigor: clear metric frameworks, experimentation standards, monitoring/alerting, and repeatable evaluation approaches for Integrity interventions. - Partner deeply with Product & Engineering to shape roadmaps, prioritize the right bets, and translate ambiguous risk signals into practical product and platform decisions. - Evolve team structure and operating model as the org scales—defining ownership boundaries, improving processes, and creating leverage through better tooling and AI-assisted workflows. - Enable cross-org outcomes, supporting partners outside Integrity (e.g., Growth, Ads, GTM) where integrity risks intersect with product and business goals. - Communicate clearly with senior leadership, synthesizing complex tradeoffs, surfacing risk, and driving alignment on priorities and success metrics. - Push the team toward an AI-leveraged operating mode, using modern tooling and model capabilities to accelerate detection, triage, analysis, and iteration. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have deep experience leading and scaling Data Science teams, ideally in trust & safety, fraud/abuse, security, risk, or other adversarial problem spaces in fast-moving environments. - Bring strong technical grounding across modern DS techniques (experimentation, causal inference, anomaly detection, risk modeling, measurement design) and can coach others to execute with rigor. - Have a track record of building durable partnerships across DS, Engineering, Product, and Operations—able to influence without authority and create shared accountability. - Are excellent at hiring, mentoring, and developing technical talent, and can build a culture that is both high-bar and supportive. - Can translate messy, evolving threats into clear frameworks, metrics, and decisions—and keep the team focused on the highest-leverage work. - Are comfortable operating in ambigu

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Apply using this link . Applications for the next cohort of Anthropic Fellows close at 11:59pm PT on July 26 . The cohort is expected to start November 2 . In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the November start date doesn't work for you. Anthropic Fellows Program overview The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent - regardless of previous experience. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis. What to expect - 4 months of full-time research - Direct mentorship from Anthropic researchers - Access to a shared workspace (in either Berkeley, California or London, UK) - Connection to the broader AI safety and security research community - Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country) - Funding for compute (~$15k/month) and other research expenses Interview process The interview process will include an initial application & reference check, technical assessments & interviews, and a research discussion. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Compensation The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (with possible extension). Fellows workstreams Due to the success of the Anthropic Fellows for AI Safety Research program, we are now expanding it across teams at Anthropic. We expect there to be significant overlap in the types of skills and responsibilities across the roles and will by default consider candidates for all the workstreams. Some of t

Scale Labs, Research Scientist — Agent Robustness As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise, Scale Labs has launched a new team focused on policy research, to bridge the gap between AI research and global policymakers to make informed, scientific decisions about AI risks and capabilities. Our research tackles the hardest problems in agent robustness, AI control protocols, and AI risk evaluations to help governments, industry, and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry, the public sector, and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision. As a Research Scientist working on Agent Robustness you will work on the fundamental challenges of building AI agents that are safe and aligned with humans. For example, you might: - Research the science of AI agent capabilities with a focus on how they relate to safety, risk factors, and methodologies for benchmarking them; - Design and build harnesses to test AI agents’ tendency to take harmful actions when pressured to do so by users or tricked into doing so by elements of their environment; - Design and build exploits and mitigations for new and unique failure modes that arise as AI agents gain affordances like coding, web browsing, and computer use; - Characterize and design mitigations for potential failure modes or broader risks of systems involving multiple interacting AI agents. Ideally you’d have: - Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance. - Practical experience conducting technical research collaboratively. You should be comfortable building and leveraging agent scaffolding, designing evaluation harnesses, and quickly turning new ideas from the research literature into working prototypes. - Experience with post-training and RL techniques such as RLHF, DPO, GRPO, and similar approaches. - A track record of published research in machine learning, particularly in generative AI. - At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development. - Strong written and verbal communication skills to operate in a cross-functional team. Nice to have: - Hands-on experience with agent evaluation frameworks such as SWE-bench, WebArena, OSWorld, Inspect, or similar tools. - Experience with red-teaming, prompt injection, or adversarial testing of AI systems. Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions. If you’re excited about advancing AI safety and contributing to our mission, we encourage you to apply, even if your experience doesn’t perfectly align with every requirement. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and m

Scale plays a vital role in the development of AI applications. Our customer base is growing exponentially, and you will be on the front lines, ensuring that the world's most innovative companies become passionate, lifelong Scale customers. Solutions Engineers partner closely with AEs, Product, and MLEs to lead prospective customers through pre-sales, delivering customized demos and pilots to secure the “technical win”. Solutions Engineers scope customer technical requirements and develop an actionable SOW. They will work closely with the delivery team to help with initial implementation. Solutions Engineers are relentlessly curious about customer needs and pain points. They employ their expert Scale product knowledge and GenAI knowledge to design solutions that best address these needs. Solutions Engineers are strong relationship builders, great project managers, and provide technical expertise. You will: - Partner with Scale AEs on the customer journey, delivering tailored demos and prototypes according to the customer's requirements. - Develop technical domain expertise in Generative AI / large language model applications for Enterprise use cases, including customers in financial services, insurance, SaaS, and similar enterprises. - Be accountable for securing the “technical win” by unblocking technical challenges - Interact with customers daily to understand their needs and design solutions to better serve them. - Design and develop “Scopes of Work” by breaking down customer challenges into a project plan - Work closely with forward-deployed Software and Machine learning Engineers to develop agents in the initial post-sales stage - Work with AEs and PMs to identify customer-specific feature requests. - Drive strategic initiatives to improve the efficiency and effectiveness of the Solution Engineering team. Ideally, you'd have: - Strong engineering background with prior experience working with clients in a pre or post-sales capacity to realize business goals. - Prior experience developing with Python, Java and/or other web development languages. - Experience working in enterprise SaaS, cloud tech, finance, fintech or similar industries in a technical capacity with end-customer engagement. - A track record as a self-starter, motivated to independently unblock technical issues in the field with the customer, away from the mothership. - Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers. - High level of comfort communicating effectively across internal and external organizations. - Intellectual curiosity, empathy, and ability to operate with high velocity. Nice to haves: - GenAI Experience - Forward deployed engineering experience - Machine Learning Experience Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equ

Scale GP (Scale Generative AI Platform) is an enterprise-grade AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our core infrastructure in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will implement solutions across multiple cloud providers (GCP, Azure, AWS) for customers in diverse, highly-regulated industries like healthcare, telecom, finance, and retail. What You’ll Do: - Architect multi-cloud systems and abstractions to allow the SGP platform to run on top of existing Cloud providers - Implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs) - Collaborate with platform, product teams and our customers directly to develop and implement innovative infrastructure that scales to meet evolving needs. - Deliver experiments at a high velocity and level of quality to engage our customers - Work across the entire product lifecycle from conceptualization through production - Be able, and willing, to multi-task and learn new technologies quickly What We’re Looking For: - 4+ years of full-time engineering experience, post-graduation - Experience scaling products at hyper growth startups - Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies - Proficient in Python or Javascript/Typescript, and SQL - Experience with Kubernetes - Experience with major cloud providers (AWS, Azure, GCP) - Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $179,400 - $224,250 USD PLEASE NOTE:&

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: We are looking for Research Engineers to help us redesign how Claude interacts with external data sources. Many of the paradigms for how data and knowledge bases are organized assume human consumers and constraints. This is no longer true in a world of LLMs! Your job will be to design new architectures for how information is organized, and train language models to optimally use those architectures. Responsibilities: - Designing and implementing from scratch new information architecture strategies - Performing finetuning and reinforcement learning to teach language models how to interact with new information architectures - Building “hard” knowledge base eval sets to help identify failure modes of how language models work with external data - Designing and evaluating advanced agentic search capabilities. You may be a good fit if you: - Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using - Have good machine learning research experience - Have experience developing software that utilizes Large Language Models such as Claude - Are results-oriented, with a bias towards flexibility and impact - Pick up slack, even if it goes outside your job description - Enjoy pair programming (we love to pair!) - Want to partner with world-class ML researchers to develop new LLM capabilities - Care about the societal impacts of your work - Have clear written and verbal communication Strong candidates will also have experience with: - Collaborating with product teams to quickly prototype and deliver innovative solutions - Building complex agentic systems that utilize LLMs - Developing scalable distributed information retrieval systems, such as search engines, knowledge graphs, RAG, indexing, ranking, query understanding, and distributed data processing The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of expe

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. Think of us as doing "neuroscience" of neural networks using "microscopes" we build - or reverse-engineering neural networks like binary programs. More resources to learn about our work: - Our research blog - covering advances including Monosemantic Features and Circuits - An Introduction to Interpretability from our research lead, Chris Olah - The Urgency of Interpretability from CEO Dario Amodei - Engineering Challenges Scaling Interpretability - directly relevant to this role - 60 Minutes segment - Around 8:07, see a demo of tooling our team built - New Yorker article - what it's like to work on one of AI's hardest open problems Even if you haven’t worked on interpretability before, the infrastructure expertise is similar to what's needed across the lifecycle of a production language model: - Pretraining: Training dictionary learning models looks a lot like model pretraining - creating stable, performant training jobs for massively parameterized models across thousands of chips - Inference: Interp runs a customized inference stack. Day-to-day analysis requires services that allow editing a model's internal activations mid-forward-pass - for example, adding a "steering vector" - Performance: Like all LLM work, we push up against the limits of hardware and software. Rather than squeezing the last 0.1%, we are focused on finding bottlenecks, fixing them and moving ahead given rapidly evolving research and safety mission The science keeps scaling - and it's now applied directly in safety audits on frontier models, with real deadlines. As our research has matured, engineering and infrastructure have become a bottleneck. Your work will have a direct impact on one of the most important open problems in AI. Responsibilities: - Build and maintain the specialized inference and training infrastructure that powers interpretability research - including instrumented forward/backward passes, activation extraction, and steering vector a

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We are looking for ML Engineers and Research Engineers to help detect and mitigate misuse of our AI systems. As a member of the Safeguards ML team, you will build systems that identify harmful use—from individual policy violations to sophisticated, coordinated attacks—and develop defenses that keep our products safe as capabilities advance. You will also work on systems that protect user wellbeing and ensure our models behave appropriately across a wide range of contexts. This work feeds directly into Anthropic's Responsible Scaling Policy commitments. Responsibilities - Develop classifiers to detect misuse and anomalous behavior at scale. This includes developing synthetic data pipelines for training classifiers and methods to automatically source representative evaluations to iterate on - Build systems to monitor for harms that span multiple exchanges, such as coordinated cyber attacks and influence operations, and develop new methods for aggregating and analyzing signals across contexts - Evaluate and improve the safety of agentic products—developing both threat models and environments to test for agentic risks, and developing and deploying mitigations for prompt injection attacks - Conduct research on automated red-teaming, adversarial robustness, and other research that helps test for or find misuse You may be a good fit if you - Have 4+ years of experience in ML engineering, research engineering, or applied research, in academia or industry - Have proficiency in Python and experience building ML systems - Are comfortable working across the research-to-deployment pipeline, from exploratory experiments to production systems - Are worried about misuse risks of AI systems, and want to work to mitigate them - Have strong communication skills and ability to explain complex technical concepts to non-technical stakeholders Strong candidates may also have experience with - Language modeling and transformers - Building classifiers, anomaly detection systems, or behavioral ML - Adversarial machine learning or red-teaming - Interpretability or probes - Reinforcement learning - High-performance, large-scale ML systems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $350,000 - $500,000 USD Logistics Minimum education: Bac

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the RL Teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators. You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will: - Invent, design and implement RL environments and evaluations. - Conduct experiments and shape our research roadmap. - Deliver your work into training runs. - Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic. You may be a good fit if you: - Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch). - Have worked across the stack – kernels, model code, distributed systems. - Know how to balance research exploration with engineering implementation. - Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: - Experience with reinforcement learning. - Experience porting ML workloads between different types of accelerators. - Familiarity with LLM training methodologies. The annual compensation range for this role is listed below. For sales roles, the range provided is th

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. As a Research Engineer on our Post-Training team, you'll train our base models through the complete post-training stack to deliver the production Claude models that users interact with. You'll work at the intersection of cutting-edge research and production engineering, implementing, scaling, and improving post-training techniques like Constitutional AI, RLHF, and other alignment methodologies. Your work will directly impact the quality, safety, and capabilities of our production models. Note: For this role, we conduct all interviews in Python. This role may require responding to incidents on short-notice, including on weekends. Responsibilities: - Implement and optimize post-training techniques at scale on frontier models - Conduct research to develop and optimize post-training recipes that directly improve production model quality - Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation - Develop tools to measure and improve model performance across various dimensions - Collaborate with research teams to translate emerging techniques into production-ready implementations - Debug complex issues in training pipelines and model behavior - Help establish best practices for reliable, reproducible model post-training You may be a good fit if you: - Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities - Adapt quickly to changing priorities - Maintain clarity when debugging complex, time-sensitive issues - Have strong software engineering skills with experience building complex ML systems - Are comfortable working with large-scale distributed systems and high-performance computing - Have experience with training, fine-tuning, or evaluating large language models - Can balance research exploration with engineering rigor and operational reliability - Are adept at analyzing and debugging model training processes - Enjoy collaborating across research and engineering disciplines - Can navigate ambiguity and make progress in fast-moving research environments Strong candidates may also: - Have experience with LLMs - Have a keen interest in AI safety and responsible deployment We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role. The annual com

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery. As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic. This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you. Key responsibilities - Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, biological sequence modeling, etc. - Partner directly with experimental biologists to design experiments that produce high-quality data, and turn results around fast enough to immediately inform the next experiment - Draw on the literature and curated biological knowledge bases alongside primary data to generate and prioritize hypotheses for experimental follow-up - Stand up and maintain the team's computational infrastructure: data ingestion, workflow orchestration, internal databases, and the interfaces that make all of it accessible to both researchers and AI agents <li class="font-claude-response-body whitespace-normal b

ABOUT THE TEAM OpenAI’s AI Success Engineer team partners with the world’s most ambitious government & partner organizations to translate cutting edge AI into real business and mission impact for governments of all levels from Local, State, Federal, and International. We guide customers and users journey from the first time they try ChatGPT Enterprise, automate a workflow, develop and execute a new skill, and create their first agent to scaled enterprise adoption of ChatGPT, Codex, our API and other novel capabilities. Our work spans technical integration and enablement, workflow transformation, inspiring and upskilling AI literacy and confidence across the workforce, sustained program, product and new capability delivery. Most importantly, we help each member of our customer's workforce, their teams, programs and missions meet their total potential. Our government customers have vital missions, and we must meet them with game-changing technology. Every engagement is an opportunity to shape how AI changes work, productivity, and innovation. This role sits at the center of that mission. ABOUT THE ROLE Governments work at a scale that is truly exponential on missions that are of critical importance to people, communities and nations. The AI Success Engineer role is the primary post-sales relationship for OpenAI’s most important customers. You are responsible for the end-to-end account management of critical Government and Partner customers. You will be helping Government Leaders/Partners appropriately and effectively use AI for their mission, while simultaneously investing in ensuring their people are AI-enabled and ready to advance positive outcomes that their constituents depend on them for. You will drive: the impact of our tools on their mission, account health and adoption, ensuring technical readiness, creating and executing on the deployment strategy, enabling, educating and training their workforce, identifying new use cases and upsell opportunities, and delivering measurable value to our customers with OpenAI’s ambitiously growing capabilities. This role blends technical depth, program and account management, customer advisory, training and enablement and product influence. You will partner deeply with customer teams, map workflows, lead configuration, oversee deployment plans, and guide customers toward high impact use cases that showcase the ways OpenAI tools can make a difference to the mission.. You drive our customers’ success and journey in an AI age. You will work closely with Sales, Solutions Architecture, Product, and Research to ensure the customer experience is connected and successful across every touchpoint. Success in this role means accelerating adoption, increasing customer use and value from our tools, guiding strategic use cases that get to production, and helping customers demonstrate tangible business and mission impact. You will bring key product feedback and insights to our product teams to ensure our capabilities continue to advance our customers' mission. IN THIS ROLE, YOU WILL: - Lead the relationship for post-sale customers and act as their trusted advisor on technical deployment, adoption, and value realization, this includes setting up, configuring and running API instances of our products. - Own customer success: account strategy & health; breadth, depth, velocity of adoption that drives mission impact, enablement and education; and ongoing technical deployment and success across your portfolio. - Be an expert in all of OpenAI products across our API and agentic platform, Codex, ChatGPT Enterprise, and more and conduct technical enablement and configuration sessions across them. - Train, educate and enable ChatGPT users to drive adoption and value. - Create and show customers how to make custom GPT’s, Skills, Agents, Plugins, Connectors, Codex and use all of the features and capabilities of our tools. - Design and lead hands-on activities like workshops, hackathons, and training sessions acr

About the Team The Personal AGI team is responsible for training and improving pre-trained models to be deployed into ChatGPT, the API, and potential future products. The team partners closely with research and product teams across the company, and conducts research as a final step to prepare for real world deployment to millions of users, ensuring that our models are safe, efficient, and reliable. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models. Our team works in research areas combining reinforcement learning and products. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Own and pursue a research agenda to improve model capability and performance. - Collaborate closely with the other research and product teams, allowing customers to optimize their own models. - Build robust evaluations for tracking modeling improvements. - Design, implement, test, and debug code across our research stack. You might thrive in this role if you: - Have a deep understanding of machine learning and machine learning applications. - Have a working knowledge of relevant models, and building evaluations for model capability improvement. - Are comfortable diving into a large ML codebase to debug. - Thrive in a dynamic and technically complex environment. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disa

ABOUT THE TEAM Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats to global security https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity. Our work involves: 1. Measurement. Monitoring and predicting the evolving capabilities of frontier AI systems. 2. Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. 3. Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework https://openai.com/index/updating-our-preparedness-framework/, and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. ABOUT THE ROLE This role leads the Automated Red Teaming (ART) effort: building scalable, research-driven systems that continuously uncover failure modes in our models and safeguards, and translate those findings into actionable, production-facing improvements. The goal is to reduce expected harm by finding the highest-leverage, least-covered weaknesses early and reliably. IN THIS ROLE, YOU'LL: - Own the research and technical direction for automated red teaming across catastrophic risk areas, with an initial emphasis on: - Automated classifier jailbreak discovery (cyber and bio). - Automated bio threat-development elicitation (worst-feasible planning uplift). - CoT monitoring evasion probing (and adjacent loss-of-control evaluations). - Partner closely with: - Vertical risk teams (Cyber, Bio, Loss of Control) to define threat models, prioritize targets, and land mitigations. - The Classifiers team to turn discovered attacks into training data, evals, and measurable robustness gains. - Product / Engineering / Safety stakeholders to ensure ART outputs are operationally useful. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Feel a strong pull toward AI safety, and you’re motivated by reducing real-world catastrophic risk (not just publishing cool results). - Love breaking systems (responsibly) — you get energy from finding weird, high-severity failure modes and turning them into concrete fixes. - Have strong applied research instincts, especially around evaluations: you’re good at designing experiments that are reproducible, interpretable, and hard to fool. - Bring hands-on experience with LLMs and agents, including multi-turn behaviors, tool use, and the ways models adapt to constraints. - Are comfortable building scalable automation, not just prototypes — you can turn red-teaming ideas into pipelines that run continuously and produce high-signal outputs. - Have solid software engineering fundamentals (data structures, algorithms, testing discipline) and you can work effectively in a production-adjacent environment. - Think in threat models and incentives, and you naturally ask “what would an attacker do next?” or “how would this fail under pressure?” - Can translate messy findings into action, communicating clearly with researchers, engineers, product, and policy — and driving alignment on what to fix first. - Care about efficiency and prioritization, and you’re happy to say “no” to low-leverage work to focus on what moves the risk needle. - Nice to have: - Experience in adversarial ML, security research / red teaming, abuse prevention systems, or large-scale eval infrastructure. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportun

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. We're seeking an exceptional Research Scientist to join our Life Sciences team at Anthropic. Our team is building a world-class research group focused on making Claude a superhuman life sciences research assistant. This role sits at the intersection of machine learning, software engineering, and biology — you'll directly improve model capabilities on scientific tasks through post-training, evaluation design, and RL environment development. As a core member of our Life Sciences team, you'll work in a high-impact team that translates deep biological domain knowledge into model training objectives, benchmarks, and agentic workflows. You'll help establish Anthropic as a leader in AI-accelerated biology while shaping how frontier models reason about and execute computational biology tasks. This role offers a unique opportunity to shape how frontier AI models learn to do biology. You'll work alongside some of the world's best AI researchers while tackling problems that matter for human health and scientific understanding. If you're excited about turning your computational biology expertise into model capabilities, we want to hear from you. Key Responsibilities - Build and ship agentic tools and integrations that let Claude execute real life science workflows — bioinformatics pipelines, database queries, analysis notebooks, literature review - Design and build evaluation benchmarks that measure model capabilities on biology tasks — figure interpretation, bioinformatics, protocol reasoning, literature synthesis - Work closely with product and design teams to scope, prototype, and ship features for life sciences users - Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements - Build and maintain the engineering infrastructure behind our biology product surface — tool scaffolding, data pipelines, eval harnesses - Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement Minimum Qualifications - Experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar - Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting — with an understanding of what real scientific workflows look like and where they break down - Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end - Hands-on experience training or fine-tuning ML models (LLMs, protein language models, or other deep learning architectures) - A track record of shipping computational tools or pipelines that biologists actually use - Comfortable navigating ambiguity and defining problems in a rapidly evolving research environme

About the Team The Proactivity Research team, within OpenAI’s broader Personal AGI team, is focused on making our models in ChatGPT and future potential products proactive in ways that are truly useful. We're laying the technical foundations for AI that can anticipate what users need in real time, adapt as their goals and preferences shift, and build a deeper, evolving understanding of the person it's helping. About the Role As a Research Engineer / Scientist, you will research and develop improvements to our models’ personalization and agentic capabilities. Our team works on reinforcement learning, dataset creation, evaluations, and other post-training methods. We partner closely with research and product teams across the company to realize the vision of a highly personalized, collaborative, and proactive assistant. We're looking for individuals with strong ML engineering skills and research experience, especially with novel and highly capable models. An ideal candidate is passionate about product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Own and pursue a research agenda to improve the proactivity and ability of our models to further user goals. - Build robust evaluations for tracking modeling improvements. - Design, implement, test, and debug code across our research stack. - Collaborate closely with the other research and product teams to influence the shape of technical solutions in the product You might thrive in this role if you: - Have a deep understanding of machine learning and machine learning applications. - Have a working knowledge of LLM post-training and evaluation approaches - Are passionate about, or have experience thinking about, personalization and enabling users to achieve their goals - Are comfortable diving into a large ML codebase to debug. - Thrive in a dynamic and technically complex environment. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and relat

Your work will change lives. Including your own. The Impact You’ll Make You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions. In This Role You Will: - Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment. - Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams. - Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards The Team You’ll Join: You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space. The Experience You Will Need: - Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems. - A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes. - Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deploy

About the Team The Personal AGI team seeks to empower all of humanity to benefit from frontier intelligence in whatever way they choose. We are responsible for training models to deploy to millions of users globally via ChatGPT, the API, and future products. We aim to evolve ChatGPT from a chatbot to an infinitely capable and personalized superassistant supporting human flourishing. We work on defining, measuring, and improving capabilities across the training stack. Our focus areas include but are not limited to model behavior, personalization, safety, factuality, instruction following, personality, interactivity, multilingual fluency, world interaction, and bringing agents to everyone. We chart the course for what to strive towards. We partner closely with research and product teams across the company ensuring that our models are safe, efficient, and reliable. About the Role You’ll work as a Research Engineer / Scientist on the North Stars team within the broader Personal AGI research org. You will work on bringing the next generation of AI-enabled experiences to all of humanity by closing the capability overhang between power users and the average consumer, including areas like tool-use, feature discovery, connectors, and instruction following. You will think deeply about the current bottlenecks in model behavior, translate these insights into robust evals, training data, reward signals, and model and harness improvements. We're looking for individuals with strong ML engineering skills and research experience passionate about creative, product-driven research. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Own and pursue a research agenda to improve model capability and performance. - Collaborate closely with the other research and product teams, allowing customers to optimize their own models. - Build robust evaluations for tracking modeling improvements. - Design, implement, test, and debug code across our research stack. You might thrive in this role if you: - Have a deep understanding of machine learning and machine learning applications. - Have a working knowledge of relevant models, and building evaluations for model capability improvement. - Are comfortable diving into a large ML codebase to debug. - Thrive in a dynamic and technically complex environment. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a condit

As an Engagement Manager on Scale's Generative AI team, you'll be the face of Scale to the world's leading AI labs and model builders — the labs training the foundational LLM and agent capabilities defining the field. You'll own these customer relationships end to end: translating what researchers need into the complex human-data programs our delivery teams execute, advocating for customer success internally, and earning Scale more work through precise, trustworthy follow-through. You'll work cross-functionally with Operations, ML, Engineering, and Finance to ensure the seamless delivery of Scale's GenAI products and services. You won't run data pipelines yourself — you own the customer relationship, shape requirements into delivery outcomes, and partner closely with the teams who execute them. The ideal candidate is organized, execution-focused, and thrives on managing complexity. Precision across tracking, documentation, and customer follow-through is what builds trust and wins repeat work. What you’ll do: - Own end-to-end account management for assigned frontier-lab customers, from opportunity scoping through project kickoff to completion. - Translate customer requirements into well-defined human-data programs run by Scale's internal delivery operations teams. - Manage customer communications, action items, and follow-ups across multiple concurrent projects with strong attention to detail. - Act as a consultative thought partner to Scale's customers, shaping their needs into clear, deliverable project outcomes. - Monitor delivery against customer throughput and quality standards, proactively flagging risks and escalating blockers with proposed solutions. - Partner cross-functionally with operations, engineering, and planning teams to improve delivery efficiency and drive better customer outcomes. What we’re looking for: - 3+ years of work experience in consulting, technical program management, or project management. - 2+ years of experience in B2B client-facing roles. - Experience in a high-growth environment, working cross-functionally and wearing multiple hats. - A technical background (education or professional experience with CS, Engineering, Economics, Mathematics, or another STEM field) - Enough understanding of the ML training lifecycle to discuss use cases meaningfully with technical customers — or a clear ability to learn it quickly. Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</em&g

The next frontier for AI is the physical world. At Scale, we're pioneering this shift, moving artificial intelligence from digital spaces into robotics. Our Robotics team builds the critical infrastructure that empowers the most sophisticated robotic efforts. We are currently seeking a strategic MLSE to join our team and make a direct impact on our platform’s success. As a Machine Learning Solutions Engineer, you'll be a trusted technical partner to the world's most innovative Foundation Model builders and renowned robotics companies. You will partner closely with Product, Sales, and ML Engineers to guide prospective customers through the pre-sales process, delivering customized demos and Proof of Concepts that secure the "technical win" as well as being a critical role in the strategy shaping of our next generation of products covering the full E2E process from data collection to deployments. You’ll help customers bridge the gap from demos to real-world deployment in production by defining technical requirements for multi-modal real-world data pipelines (from data collection, curation, training to deploying models at customer sites and evaluating the performance). You'll develop actionable Statements of Work and collaborate with the delivery team on high-fidelity ground truth implementation. Your expert knowledge of Scale's products will allow you to design creative, impactful solutions. This is a critical role that directly influences multi-million dollar contracts and initiatives. You'll travel globally to conduct on-site technical workshops and scope new projects, while also leading demos and pilots for new prospects. You'll be part of a tight-knit, specialized team, influencing a rapidly growing business that is expanding into new product areas. In this role, you will: - Partner with Scale Account Executives and Engagement Managers to deliver new customer pilots and grow technical relationships with existing clients. - Work with Product Engineering and Product Management to influence our product roadmap based on your frontline insights and help implementing and developing PoC’s - Become a domain expert in next-generation Robotics and physical AI (e.g. VLMs, VLAs, World Models) - Develop technical domain expertise in areas of 2D and 3D imaging and annotation, multi-sensor fusion and calibration, GPS/INS navigation systems, computer vision and other autonomy-adjacent concepts - Be accountable for the technical customer experience and commercial growth, expanding relationships and use cases with existing customers. - Collaborate with highly technical engineers at our customer sites to ensure satisfaction with our data, software platforms, and workflows. - Design and develop playbooks, demos, and other tools to ensure efficient and successful pilots and customer expansions. - Pioneer the development of a global Robotics Data Marketplace, actively seeking out and engaging with key international partners to build a comprehensive data ecosystem. - Evangelize Scale by interacting with customers at major industry events and academic conferences. You have: - PhD in Robotics, or an M.S. in Robotics with a strong track record of deploying VLAs to production. - 3+ years of experience developing with Python, C++, Java, and/or other scripting languages. - Exceptional project management and interpersonal skills, strong attention to detail, and a strong sense of ownership. - The presentation skills and technical credibility to speak confidently with a variety of stakeholders, from execu

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Full-Stack Software Engineer in RL, you'll build the platforms, tools, and interfaces that power environment creation, data collection, and training observability. The quality of Claude's next generation depends on the quality of the data we train it on — and the systems you build are what make that data possible. You'll own product surfaces end-to-end — from backend services and APIs to the web UIs that researchers, external vendors, and thousands of data labelers use every day. You don't need a background in ML research. What matters is that you can take an ambiguous, high-stakes problem and ship a polished, reliable product against it, fast. This team moves very quickly. Claude writes a lot of the code we commit, which means the bottleneck isn't typing — it's judgment, taste, and the ability to react to what researchers need next. You'll iterate on data collection strategies to distill the knowledge of thousands of human experts around the world into our models, and you'll do it in a loop that closes in hours and days, not quarters or months. Anthropic's Reinforcement Learning organization leads the research and development that trains Claude to be capable, reliable, and safe. We've contributed to every Claude model, with significant impact on the autonomy and coding capabilities of our most advanced models. Our work spans teaching models to use computers effectively, advancing code generation through RL, pioneering fundamental RL research for large language models, and building the scalable training methodologies behind our frontier production models. The RL org is organized around four goals: solving the science of long-horizon tasks and continual learning, scaling RL data and environments to be comprehensive and diverse, automating software engineering end-to-end, and training the frontier production model. Our engineering teams build the environments, evaluation systems, data pipelines, and tooling that make all of this possible — from realistic agentic training environments and scalable code data generation to human data collection platforms and production training operations. What You'll Do - Build and extend web platforms for RL environment creation, management, and quality review — including environment configuration, versioning, and validation workflows - Develop vendor-facing interfaces and tooling that let external partners create, submit, and iterate on training environments with minimal friction - Design and implement platforms for human data collection at scale, including labeling workflows, quality assurance systems, and feedback mechanisms that surface reward signal integrity issues early - Build evaluation dashboards and observability UIs that give researchers real-time insight into environment quality, training run health, and reward hacking - Create backend services and APIs that connect environment authoring tools, data collection systems, and RL training infrastructure - Build and expand scalable code data generation pipelines, producing diverse programming tasks with robust reward signals across languages and difficulty levels - Develop onboarding automation and documentation tooling so new vendors and internal users r

About the Team The Integrity team at OpenAI is dedicated to ensuring that our cutting-edge technology is not only revolutionary, but also secure from a myriad of adversarial threats. We strive to maintain the integrity of our platforms as they scale. The Integrity team is at the front lines of defending against misuse in all its forms: content abuse, scaled attacks, and other actions that could undermine the user experience or harm our operational stability. About the Role As a Machine Learning Engineer in OpenAI's Integrity team, you will have the opportunity to work with some of the brightest minds in AI. You’ll work on state-of-the-art models and classifiers, experiment with new architecture and approaches, and push forward our abilities in content and user understanding. You’ll help turn research breakthroughs into tangible solutions that improve the trust and safety of our platform. If you're excited about training LLMs and building ML models, this role is your chance to make a significant mark. In this role, you will: - Innovate and Deploy: Design and deploy advanced machine learning models that solve real-world problems. Bring OpenAI's research from concept to implementation, creating AI-driven applications with a direct impact. - Collaborate with the Best: Work closely with researchers, software engineers, and product managers to understand complex business challenges and deliver AI-powered solutions. Be part of a dynamic team where ideas flow freely and creativity thrives. - Optimize and Scale: Implement scalable data pipelines, optimize models for performance and accuracy, and ensure they are production-ready. Contribute to projects that require cutting-edge technology and innovative approaches. - Learn and Lead: Stay ahead of the curve by engaging with the latest developments in machine learning and AI. Take part in code reviews, share knowledge, and lead by example to maintain high-quality engineering practices. - Make a Difference: Monitor and maintain deployed models to ensure they continue delivering value. Your work will directly influence how AI benefits individuals, businesses, and society at large. You might thrive in this role if you: - Master's/ PhD degree in Computer Science, Machine Learning, Data Science, or a related field. - Demonstrated experience in deep learning and transformers models - Experience with content understanding or abuse prevention with LLMs is a plus - Proficiency in frameworks like PyTorch or Tensorflow - Strong foundation in data structures, algorithms, and software engineering principles. - Are familiar with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization - Excellent problem-solving and analytical skills, with a proactive approach to challenges. - Ability to work collaboratively with cross-functional teams. - Ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines - Enjoy owning the problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employm

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a member of Agent Post-Training, Computer Use, you will teach models to operate computers. You will help train models that can navigate browsers and desktops, use tools and applications, reason through complex workflows, collaborate with users and other agents, and complete long-horizon tasks with reliability and judgment. This work sits at the intersection of frontier model training, product behavior, evaluation, and systems engineering, and will directly shape the computer-use capabilities shipped in OpenAI’s next generation of agents. Currently, our models are the best in the world at this behavior! You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT - Design and run experiments that improve agentic model behavior for complex computer use https://openai.com/index/codex-for-almost-everything/, including desktop and browser. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. YOU MIGHT THRIVE IN THIS ROLE IF YOU - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution. - Care about product impact and model behavior, n

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval https://openai.com/index/gdpval/, SWE-bench Verified https://openai.com/index/introducing-swe-bench-verified/, MLE-bench https://openai.com/index/mle-bench/, PaperBench https://openai.com/index/paperbench/, and SWE-Lancer https://openai.com/index/swe-lancer/. If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT - Create ambitious RL environments to push our models to their limits, and measure frontier model capabilities, skills, and behaviors - Develop new methodologies for automatically exploring the behavior of these models - Dive deep into the science of measurement, including understanding scalability, reliability, and variance of our evaluation methodology - Help steer training for our largest training runs, and see the future first - Design scalable systems and processes to support continuous evaluation - Build self-improvement loops to automate model understanding YOU MIGHT THRIVE IN THIS ROLE IF YOU - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution. - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with. - Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next. - Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group. - Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous. - Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users. Abo

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team builds the data, environments, graders, training methods, and feedback loops that shape what OpenAI’s next agents can do and what they are like to work with, then carries those improvements through major training runs and into products used by people every day. ABOUT THE ROLE As a member of the Agent Post-training Personality team, you will help make OpenAI’s agents exceptional collaborators. You will study what makes an agent thoughtful, clear, perceptive, appropriately proactive, and genuinely easy to work with, then translate those insights into evals, training data, reward signals, and model improvements. We use “personality” to mean much more than writing style or general likability. It includes whether an agent understands what the user is trying to accomplish, communicates with good judgment, adapts to context, asks useful questions, handles disagreement honestly and takes initiative at the right moments. The goal is to create a strong, tasteful default that can adapt to different people and situations. This work combines behavioral research, product thinking, research and communication taste. You will collaborate with product teams, human experts, and researchers across post-training and pretraining to ensure that improvements survive the full training stack and reach the models people use every day. IN THIS ROLE, YOU MIGHT - Develop a rigorous understanding of what makes an agent a great collaborator across professional, creative, technical, and everyday work. - Turn qualitative judgments about model behavior into concrete hypotheses, evals, graders, and training interventions. - Study explicit and implicit user signals to understand which behaviors create trust, satisfaction, continued use, and successful outcomes. - Work with human experts and trainers to produce high-quality, tasteful rollouts and preference data that capture excellent collaborative behavior. - Improve reward models and RL objectives for model behaviors. - Work with pretraining and early-training teams on data mixtures, objectives, synthetic data, and other upstream choices that shape downstream personality. - Build sustainable pipelines for updating older training data as our understanding of excellent model behavior evolves. - Partner closely with ChatGPT, Codex, and other product teams to turn consumer insight into model improvements and validate them in real workflows. - Own projects end to end, from observing a subtle behavioral failure through experimentation, training, evaluation, and launch. YOU MIGHT THRIVE IN THIS ROLE IF YOU - Think instinctively from the user’s perspective and care deeply about how models feel to work with, not only how they perform on benchmarks. - Can translate subjective-seeming product questions into falsifiable hypotheses and rigorous evaluations without losing the nuance that made the question important. - Care about preserving individuality, adaptability, and behavioral diversity rather than optimizing every model toward one narrow style. - Want to shape how frontier agents communicate, collaborate, and build trust with millions of people. - Have strong technical foundations in machine learning, software engineering, statistics, behavioral science, HCI, or a related field, and can quickly learn across u

About the Team The Support Automation team at OpenAI scales the organization by applying cutting-edge AI models to real-world challenges, automating and enhancing work across the organization. From customer operations to engineering, we develop an ecosystem of automation products that empower our colleagues and drive impact. We're passionate about crafting products that serve those around us, blending rapid prototyping with a focus on long-term quality and reliability. By creating reusable solutions, we create patterns that can be applied across diverse domains within OpenAI. TLDR: this team leverages OpenAI technology to improve OpenAI, and you’ll have the opportunity to leverage the full extent of our tech (both public and pre-released) to accomplish this mission. About the Role We’re looking for a Backend Software Engineer with experience working in ML/LLM-heavy domains to help to design and build an evals infrastructure that measures the quality of OpenAI’s support automation. This is a deeply technical and highly cross-functional role where you’ll build robust systems and backend services that serve as the foundation for how knowledge is created, accessed, and applied across OpenAI. The role will especially focus on working closely with Data Science and Research partners to design and build evals at scale. In this role, you will: - Design eval pipelines that are reliable, reproducible, and extendable - Build the infrastructure for continuous eval monitoring frameworks (regression/drift monitoring, building robust golden datasets) along with feedback loops that ultimately strengthen support automation - Design, build, and maintain backend services and APIs to support intelligent automation and knowledge systems - Integrate and structure data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows. - Collaborate closely with data, research, and engineering teams to integrate OpenAI models into high-leverage workflows - Own the full development lifecycle of new backend systems and internal platform capabilities - Build with scale and maintainability in mind, while rapidly iterating on new ideas You might be a great fit if you have: - 4+ years of backend engineering experience at product-driven companies (excluding internships) - Proficiency in backend technologies. Our tech stack includes Python, FastAPI, and Postgres - Experience designing and scaling distributed systems, APIs, or data processing pipelines - Have experience building AI agents or applications, including designing evals and improving performance through prompting or scaffolding - Are familiar with evaluation methods for LLMs and have worked with patterns like multi-agent workflows, tool use, or long context. - Experience creating production evals and/or measuring performance of ML/LLM models at scale - A pragmatic mindset. You’re comfortable shipping iteratively while building toward a long-term vision About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordan

About the Team The OpenAI Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role As a Research Engineer, Distributed Data Systems, you will design and scale the infrastructure that powers large-scale multimodal training and evaluation at OpenAI. You’ll manage distributed data pipelines, collaborate closely with researchers to translate requirements into robust systems, and harden pipelines that serve as the backbone for OpenAI's rapid iteration cycles. We’re looking for engineers who are detail-oriented, have strong experience with distributed systems, and excel at building reliable infrastructure in high-stakes environments. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, machine learning infrastructure while ensuring scalability, reliability, and security. - Ensure our data platform can scale by orders of magnitude while remaining reliable and efficient. - Partner with researchers to deeply understand requirements and translate them into production-ready systems. - Harden, optimize, and maintain critical data infrastructure systems that power multimodal training and evaluation. You might thrive in this role if you: - Have strong experience with distributed systems and large-scale infrastructure with a strong interest in data. - Are detail-oriented and bring rigor to building and maintaining reliable systems. - Demonstrate excellent software engineering fundamentals and organizational skills. - Are comfortable with ambiguity and rapid change. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information

ABOUT THE TEAM OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. ABOUT THE ROLE We’re seeking a Research-Hardware Codesign Engineer to operate at the boundary between model research and silicon/system architecture. You’ll help shape the numerics, architecture, and technology bets of future OpenAI silicon in collaboration with both Research and Hardware. Your work will include debugging gaps between rooflines and reality, writing quantization kernels, derisking numerics via model evals, quantifying system architecture tradeoffs, and implementing novel numeric RTL. This is a hands-on role for people who go looking for hard problems, get to ground truth, and drive it to production. Strong prioritization and clear, honest communication are essential. Location: San Francisco, CA (Hybrid: 3 days/week onsite) Relocation assistance available. IN THIS ROLE YOU WILL: - Build on our roofline simulator to track evolving workloads, and deliver analyses that quantify the impact of system architecture decisions and support technology pathfinding. - Debug gaps between performance simulation and real measurements; clearly communicate root cause, bottlenecks, and invalid assumptions. - Write emulation kernels for low-precision numerics and lossy compression schemes, and get Research the information they need to trade efficiency with model quality. - Prototype numerics modules by pushing RTL through synthesis; hand off novel numerics cleanly, or occasionally own an RTL module end-to-end. - Proactively pull in new ML workloads, prototype them with rooflines and/or functional simulation, and drive initial evaluation of new opportunities or risks. - Understand the whole picture from ML science to hardware optimization, and slice this end-to-end objective into near-term deliverables. - Build ad-hoc collaborations across teams with very different goals and areas of expertise, and keep progress unblocked. - Communicate design tradeoffs clearly with explicit assumptions and confidence levels; produce a trail of evidence that enables confident execution. YOU WILL THRIVE IN THIS ROLE IF: - An exceptional track record of high-quality technical output, and a bias for shipping a prototype now and iterating later in the absence of clear requirements. - Strong Python, and C++ or Rust, with a cautious attitude toward correctness and an intuition for clean extensibility. - Experience writing Triton, CUDA, or similar, and an understanding of the resulting mapping of tensor ops to functional units. - Working knowledge of PyTorch or JAX; experience in large ML codebases is a plus. - Practical understanding of floating point numerics, the ML tradeoffs of reduced precision, and the current state of the art in model quantization. - Deep understanding of transformer models, and strong intuition for transformer rooflines and the tradeoffs of sharded training and inference in large-scale ML systems. - Experience writing RTL (especially for floating point logic) and understanding of PPA tradeoffs is a plus. - Strong cross-functional communication (e.g. across ML researchers and hardware engineers); ability to slice ambiguous early-incubation ideas into concrete arenas in which progress can be made. To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries

About the Team OpenAI’s mission is to build safe artificial general intelligence (AGI) that benefits all of humanity. Achieving this requires bringing the world’s most exceptional talent under one roof to push the boundaries of what’s possible. Our Research Recruiting team plays a critical role in this effort. We are an embedded part of the research organization, working side by side with our research staff to deeply understand evolving priorities, build trust, and strategically shape the future of OpenAI’s talent. About the Role You will own and execute long-term talent strategies to identify, engage, and recruit many of the world’s leading and emerging AI researchers, research engineers, and technical scientists working at the frontier of machine learning. This is not a traditional execution-focused recruiting role. You will operate as a strategic partner to OpenAI’s research staff, helping define hiring priorities, shape search strategy, influence candidate evaluation, and guide hiring decisions that directly impact the direction and quality of our frontier-model research and fulfillment of our mission. In this role, you will: - Partner directly with research and technical staff to define hiring priorities, shape search strategies, and anticipate future talent needs as technical roadmaps evolve. - Proactively identify and cultivate exceptional AI/ML research talent across industry, academia, and emerging labs, often before formal hiring needs exist. - Use market insights and candidate signals to influence hiring decisions, leveling, and compensation strategy for highly specialized research roles. - Serve as a trusted advisor throughout candidate evaluation and closing — helping leaders calibrate for research excellence, long-term potential, and organizational fit. - Collaborate closely with your sourcing partner to execute complex, high-impact searches in ambiguous or rapidly evolving technical domains. You might thrive in this role if you: - Significant experience recruiting within highly technical or specialized environments. - Deep interest in AI research and a desire to engage directly with global research communities. - Experience recruiting within highly technical or specialized environments such as ML/AI, distributed systems, infrastructure, scientific computing, or quantitative research. - Track record of leading complex, ambiguous technical searches from early talent mapping through close. - Experience navigating high-stakes negotiations with senior technical or research candidates. - Comfort operating in fast-moving environments where hiring priorities and role definitions may evolve over time. Workplace & Location This role is based in our San Francisco office and we aren’t considering remote applications at this time. We use a hybrid work model of 3 days in the office with optional work from home on Thursdays and Fridays. We also offer relocation assistance to new employees. Our open-plan offices have height-adjustable desks, conference rooms, phone booths, well-stocked kitchens full of snacks and drinks, three in-house prepared meals daily, outdoor space for working and socializing, wellness rooms, private bike storage, and more. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional i

About the Team OpenAI’s mission is to build safe artificial general intelligence (AGI) that benefits all of humanity. Achieving this requires bringing the world’s most exceptional talent under one roof to push the boundaries of what’s possible. Our Research Recruiting team plays a critical role in this effort. We are an embedded part of the research organization, working side by side with our research staff to deeply understand evolving priorities, build trust, and strategically shape the future of OpenAI’s talent. About the Role You will own and execute long-term talent strategies to identify, engage, and recruit many of the world’s leading and emerging AI researchers, research engineers, and technical scientists working at the frontier of machine learning. This is not a traditional execution-focused recruiting role. You will operate as a strategic partner to OpenAI’s research staff, helping define hiring priorities, shape search strategy, influence candidate evaluation, and guide hiring decisions that directly impact the direction and quality of our frontier-model research and fulfillment of our mission. In this role, you will: - Partner directly with research and technical staff to define hiring priorities, shape search strategies, and anticipate future talent needs as technical roadmaps evolve. - Proactively identify and cultivate exceptional AI/ML research talent across industry, academia, and emerging labs, often before formal hiring needs exist. - Use market insights and candidate signals to influence hiring decisions, leveling, and compensation strategy for highly specialized research roles. - Serve as a trusted advisor throughout candidate evaluation and closing — helping leaders calibrate for research excellence, long-term potential, and organizational fit. - Collaborate closely with your sourcing partner to execute complex, high-impact searches in ambiguous or rapidly evolving technical domains. You might thrive in this role if you: - Significant experience recruiting within highly technical or specialized environments. - Deep interest in AI research and a desire to engage directly with global research communities. - Experience recruiting within highly technical or specialized environments such as ML/AI, distributed systems, infrastructure, scientific computing, or quantitative research. - Track record of leading complex, ambiguous technical searches from early talent mapping through close. - Experience navigating high-stakes negotiations with senior technical or research candidates. - Comfort operating in fast-moving environments where hiring priorities and role definitions may evolve over time. Workplace & Location This role is based in our London office and we aren’t considering remote applications at this time. We use a hybrid work model of 3 days in the office with optional work from home on Thursdays and Fridays. We also offer relocation assistance to new employees. Our open-plan offices have height-adjustable desks, conference rooms, phone booths, well-stocked kitchens full of snacks and drinks, three in-house prepared meals daily, outdoor space for working and socializing, wellness rooms, private bike storage, and more. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional informat

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As Data Operations Manager, you'll build and scale data operations across research teams working on frontier AI capabilities. You'll partner with researchers to design and execute data strategies, manage vendor relationships, and own the entire data pipeline from requirements to production. This role requires operational excellence combined with technical depth to understand what makes high-quality training data, but your focus will be on strategy and execution. About the Impact The data operations you build will directly determine how well our models perform on critical capabilities—tool use accuracy, prompt injection robustness, long-horizon reasoning, and safety alignment. You'll work with world-class researchers advancing the frontier while building the operational infrastructure to scale these efforts. We're looking for someone who gets excited about the challenge of scaling quality across diverse research areas—someone who can understand nuanced technical requirements, build the right partnerships, and execute flawlessly. If you thrive at the intersection of operational excellence and cutting-edge AI research, we'd love to hear from you. Responsibilities: - Own and execute data strategy for research teams advancing frontier AI capabilities across RLHF, safety, tool use, and agentic workflows - Drive strategic vendor partnerships and build scalable frameworks for technical data collection at scale - Design and implement operational systems that translate research requirements into high-quality data pipelines - Build evaluation frameworks and quality standards that ensure data meets the bar for training state-of-the-art AI systems - Lead cross-functional initiatives to optimize research velocity while maintaining rigorous quality standards - Proactively identify risks, bottlenecks, and opportunities to improve efficiency and effectiveness across data operations - Partner with senior research leaders to align data operations with model development roadmaps and strategic priorities You may be a good fit if you: - Have 3+ years in operations, consulting, product management, or program management roles - Have exceptional project management skills with ability to handle multiple complex projects simultaneously - Have strong communication skills and can engage effectively with technical and non-technical stakeholders - Are familiar with how LLMs work or have strong interest in understanding AI training methodologies - Are highly organized and can navigate ambiguity effectively - Have experience with data analysis tools (SQL, Python, Tableau, spreadsheets, or similar) - Thrive in fast-paced research environments with shifting priorities - Are passionate about AI safety an

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As part of our growing Data Science & Analytics team, you will play an instrumental role in Anthropic's mission of building safe and beneficial AI — this time by driving data-informed decisions across the commercial customer lifecycle. This role sits at the intersection of fast-moving sales operations and rigorous statistical analysis. You will work across multiple segments and products, partnering with analytics engineers, fellow data scientists, and go-to-market leadership to turn complex commercial data into actionable strategy. You will own measurement and analysis for new logo acquisition through activation, expansion, and retention for a rapidly scaling, consumption-based AI platform. You've worked in cultures of analytical rigor before, and you're eager to help shape the norms and best practices of a growing data science function at a pivotal moment in the company's growth. Key responsibilities - Define key metrics, build measurement frameworks, and maintain core reporting to evaluate GTM success across segments and products - Analyze commercial and user data to surface actionable insights, size opportunities, and influence roadmaps and go-to-market strategy - Develop hypotheses and apply rigorous causal inference methods — controlled experiments, synthetic controls — to make clear, actionable recommendations - Investigate anomalies, conduct root cause analyses, and provide data-driven guidance on priorities and decisions - Build statistical models, optimization frameworks, and simulations to support and automate commercial decision-making processes - Present analyses and recommendations to both technical and non-technical stakeholders, including GTM leadership - Establish foundational data practices and help scale analytics infrastructure to support rapid product and commercial iteration Minimum qualifications - Proficiency in Python, SQL, and data visualization tools - Expertise in experimental design, causal inf

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic is looking for an Engineering Manager to lead the Agent Prompts & Evals team. This team owns the infrastructure that lets Anthropic ship model and prompt changes with confidence — the eval frameworks, system prompt pipelines, and regression-detection systems that every model launch depends on. When a new Claude model is ready to ship, this team is the one answering “is it actually better in our products?” When a product team wants to change how Claude behaves, this team owns the tooling that tells them whether they broke something. It’s a platform team whose platform is model behavior itself. The team sits deliberately at the seam between product engineering and research. You’ll partner closely with other evals groups across the company on shared infrastructure and methodology, with product teams who are shipping features on top of Claude, and with the TPMs and research PMs driving model launches. The pace is set by the model release cadence, and the team operates as both a platform owner and a hands-on partner during launch periods. You don’t need a research background, but you do need to want to learn how to measure things like “is Claude being too sycophantic” or “did web search get worse.” The best version of this role is someone who’s built strong platform or devtools teams before and is excited to apply that skillset to a domain where the thing you’re measuring is a language model. Responsibilities - Lead and grow a team of prompt engineers and platform software engineers - Own the product-side eval platform: the frameworks, dashboards, bulk runners, and CI integrations that product teams use to measure Claude’s behavior and catch regressions before they ship - Own system prompt infrastructure: versioning, deployment, rollback, and review tooling for the prompts that run in production across claude.ai , the API, and agentic surfaces - Be a steady hand through model launches — these are the team’s highest-stakes operational moments and the EM is the backstop when things get chaotic - Build durable collaboration with other evals groups across the company; this means real work on ownership boundaries, shared roadmaps, and avoiding tragedy-of-the-commons on shared eval infrastructure - Recruit, close, and retain engineers who want to work at the intersection of product engineering and model behavior - Shape where the team invests next: there are credible paths into frontier eval development, model launch automation, and deeper prompt engineering support, and part of the job is sequencing them - Push the team toward measuring things that are hard to measure — behavioral drift, prompt quality, harness parity — not just things that are easy You May Be a

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the RL Teams Our Reinforcement Learning teams play a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of our latest Claude models. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to write, edit, test, debug, and ship real software — end to end, on real codebases, with real tools — and to do it correctly, fast, and safely. This role blends research and engineering. You'll design RL environments and coding tasks, build the reward signals and verifiers that capture what "good code" means, run training experiments on frontier models, diagnose why a model does (or doesn't) get better at a class of software-engineering work, and improve the speed and reliability of the pipelines that make all of that iterate fast. Code RL spans several focus areas — from agentic coding behaviors and code correctness, to long-horizon autonomous engineering, to high-performance code for accelerators — and we'll match you to the area where you'll have the most impact. You may be a good fit if you: - Have strong software-engineering skills and deep Python expertise, including async/concurrent programming - Are comfortable owning systems end to end and debugging across the stack - Can balance research exploration with engineering implementation, and engage rigorously in shaping experimental design and interpreting results - Care about code quality, testing, and performance - Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Strong candidates may also have: - Experience with reinforcement learning, RLHF, post-training, or LLM finetuning - Built coding agents, code-execution sandboxes, eval harnesses, veri

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Computer Use team focuses on teaching Claude to see, use, and understand computer interfaces. As a Research Engineer on the team, you'll work on advancing our models' ability to reliably and safely operate real software. We're looking for someone who's genuinely excited about both the research and the product sides of computer use. Your work will translate directly into model improvements in our own and our customers' products. You can try Claude's computer use capabilities today through the Claude in Chrome extension and Claude Cowork. Key Responsibilities: - Design and run experiments to improve Claude's perception and agentic capabilities - Develop robust, reliable evaluation frameworks for measuring our models' ability to complete complex computer tasks - Build and improve computer use and vision reinforcement learning training environments - Create pipelines and tools to test and validate complex RL environments - Collaborate with teams across the model training and infrastructure stack to improve our production training setup - Partner with product teams to bring research advances into production Minimum Qualifications: - Software engineering experience and proficiency in Python - Experience training, fine-tuning, or evaluating machine learning models - Strong communication skills and a collaborative working style - Care about the societal impacts and safety of your work Preferred Qualifications: - Experience training models for computer use or other agentic capabilities - Experience with reinforcement learning, particularly in long-horizon or sparse-reward settings - Familiarity with multimodal model training - Experience building evaluations or benchmarks for agentic systems - Experience building reinforcement learning environments, simulation systems, or large-scale ML infrastructure - Experience working closely with product teams to drive model improvements The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experie

Scale AI's Advisory practice is the most forward-leaning bet in our go-to-market motion. We're not waiting for clients to hand us a scope — we get in front of the decision, shape the right AI problems before a build begins, and make Scale the obvious partner for what comes next. Advisory is the structured answer to the question enterprises are already asking us: "What should we build, and where do we start?" It's a discovery and scoping phase that runs before delivery — designed to land bigger contracts, drive more predictable execution, and accelerate expansion. By 2027, it will be Scale's default first engagement with new enterprise clients. As an AI Advisory Consultant, you sit at the center of every engagement — paired with a Principal, producing the research, synthesis, impact sizing, and workshop materials that shape the work, and pressure-testing the team's assumptions. You also run the engagement day-to-day — owning the timelines, trackers, and coordination across Solutions Engineering and Design, and acting as the single source of truth that keeps everything on track from kickoff to final readout. This is a delivery role; you own real parts of the advisory work and partner closely with the Principal to deliver the engagement. It's a direct path into the AI Advisory Principal track. What You'll Own You're at the center of every engagement — doing the work and keeping it on track. - Deliver the core advisory work. You produce the output the engagement runs on — market research, impact sizing, synthesis, and workshop materials — working alongside the Principal. - Run the engagement day-to-day. You own the timelines, trackers, and single source of truth — monitoring open items and follow-ups across Solutions Engineering and Design, and keeping the engagement on track from kickoff to final readout. Working closely with the Solutions Engineering workstream, you weave their technical and feasibility findings into the readouts so the engagement tells one coherent story. - Build the case ahead of client conversations. You develop the research and supporting materials that set up pre-sales conversations to land, and support the Principal in building out vertical thought leadership. - Turn each engagement into reusable knowledge. You own the debrief notes, findings write-ups, and structured hand-offs after each engagement, and you contribute the patterns and materials that improve the motion and playbook. - Build the AI workflows that make us faster. You spot where the team's work can be automated or templatized — recaps, status tracking, synthesized readouts, reusable materials — and you build those AI-powered solutions, not just flag them. You also set up baseline measurement and tracking. Qualifications Ideally, you’d have - Consultant equivalent experience at a Tier 1 firm (McKinsey, Bain, BCG ideally); 3+ years of work experience. Experience on AI strategy or transformation projects strongly preferred. - A track record of structured, analytics-driven problem solving — turning messy, ambiguous inputs into a clear, defensible point of view. - Excellent communication — produces clean, exec-ready first drafts of findings and materials that need little editing. - A history of diligence and organization across multiple workstreams — owns the trackers and details so nothing slips, even under time pressure. - Technical credibility. No engineering degree required. You are curious enough to push bac

About the Team Data Platform at OpenAI owns the foundational data stack powering critical product, research, and analytics workflows. We operate some of the largest Spark compute fleets in production; design, and build data lakes and metadata systems on Iceberg and Delta with a vision toward exabyte-scale architecture; run high throughput streaming platforms on Kafka and Flink; provide orchestration with Airflow; and support ML feature engineering tooling such as Chronon. Our mission is to deliver reliable, secure, and efficient data access at scale and accelerate intelligent, AI assisted data workflows. Join us to build and operate these core platforms that underpin OpenAI products, research, and analytics. We’re not just scaling infrastructure – we’re redefining how people interact with data. Our vision includes intelligent interfaces and AI-assisted workflows that make working with data faster, more reliable, and more intuitive. About the Role This role focuses on building and operating data infrastructure that supports massive compute fleets and storage systems, designed for high performance and scalability. You’ll help design, build, and operate the next generation of data infrastructure at OpenAI. You will scale and harden big data compute and storage platforms, build and support high-throughput streaming systems, build and operate low latency data ingestions, enable secure and governed data access for ML and analytics, and design for reliability and performance at extreme scale. You will take full lifecycle ownership: architecture, implementation, production operations, and on-call participation. You’ve supported Spark, Kafka, Flink, Airflow, Trino, or Iceberg as platforms. You’re well-versed in infrastructure tooling like Terraform, experienced in debugging large-scale distributed systems, and excited about solving data infrastructure problems in the AI space. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design, build, and maintain data infrastructure systems such as distributed compute, data orchestration, distributed storage, streaming infrastructure, machine learning infrastructure while ensuring scalability, reliability, and security - Ensure our data platform can scale by orders of magnitude while remaining reliable and efficient - Accelerate company productivity by empowering your fellow engineers & teammates with excellent data tooling and systems - Collaborate with product, research and analytics teams to build the technical foundations capabilities that unlock new features and experiences - Own the reliability of the systems you build, including participation in an on-call rotation for critical incidents You might thrive in this role if you: - Have 4+ years in data infrastructure engineering OR - Have 4+ years in infrastructure engineering with a strong interest in data - Take pride in building and operating scalable, reliable, secure systems - Are comfortable with ambiguity and rapid change - Have an intrinsic desire to learn and fill in missing skills, and an equally strong talent for sharing learnings clearly and concisely with others This role is exclusively based in our San Francisco HQ. We offer relocation assistance to new employees. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex,

This role will support the fleet infrastructure team at OpenAI. The fleet team focuses on running the world’s largest, most reliable, and frictionless GPU fleet to support OpenAI’s general purpose model training and deployment. Work on this team ranges from - Maximizing GPUs doing useful work by building user-friendly scheduling and quota systems - Running a reliable and low maintenance platform by building push-button automation for kubernetes cluster provisioning and upgrades - Supporting research workflows with service frameworks and deployment systems - Ensuring fast model startup times though high performance snapshot delivery across blob storage down to hardware caching - Much more! About the Role As an engineer within Fleet infrastructure, you will design, write, deploy, and operate infrastructure systems for model deployment and training on one of the world’s largest GPU fleet. The scale is immense, the timelines are tight, and the organization is moving fast; this is an opportunity to shape a critical system in support of OpenAI's mission to advance AI capabilities responsibly. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will: - Design, implement and operate components of our compute fleet including job scheduling, cluster management, snapshot delivery, and CI/CD systems. - Interface with researchers and product teams to understand workload requirements - Collaborate with hardware, infrastructure, and business teams to provide a high utilization and high reliability service You might thrive in this role if you: - Have experience with hyperscale compute systems - Possess strong programming skills - Have experience working in public clouds (especially Azure) - Have experience working in Kubernetes - Execution focused mentality paired with a rigorous focus on user requirements - As a bonus, have an understanding of AI/ML workloads About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data sec

About the Team The Frontier Systems team at OpenAI builds, launches, and supports the largest supercomputers in the world that OpenAI uses for its most cutting edge model training. We take data center designs, turn them into real, working systems and build any software needed for running large-scale frontier model trainings. Our mission is to bring up, stabilize and keep these hyperscale supercomputers reliable and efficient during the training of the frontier models. About the Role We are looking for engineers to operate the next generation of compute clusters that power OpenAI’s frontier research. This role blends distributed systems engineering with hands-on infrastructure work on our largest datacenters. You will scale Kubernetes clusters to massive scale, automate bare-metal bring-up, and build the software layer that hides the complexity of a magnitude of nodes across multiple data centers. You will work at the intersection of hardware and software, where speed and reliability are critical. Expect to manage fast-moving operations, quickly diagnose and fix issues when things are on fire, and continuously raise the bar for automation and uptime. In this role, you will: - Spin up and scale large Kubernetes clusters, including automation for provisioning, bootstrapping, and cluster lifecycle management - Build software abstractions that unify multiple clusters and present a seamless interface to training workloads - Own node bring-up from bare metal through firmware upgrades, ensuring fast, repeatable deployment at massive scale - Improve operational metrics such as reducing cluster restart times (e.g., from hours to minutes) and accelerating firmware or OS upgrade cycles - Integrate networking and hardware health systems to deliver end-to-end reliability across servers, switches, and data center infrastructure - Develop monitoring and observability systems to detect issues early and keep clusters stable under extreme load You might thrive in this role if you: - Have deep experience operating or scaling Kubernetes clusters or similar container orchestration systems in high-growth or hyperscale environments - Bring strong programming or scripting skills (Python, Go, or similar) and familiarity with Infrastructure-as-Code tools such as Terraform or CloudFormation - Are comfortable with bare-metal Linux environments, GPU hardware, and large-scale networking - Enjoy solving fast-moving, high-impact operational problems and building automation to eliminate manual work - Can balance careful engineering with the urgency of keeping mission-critical systems running Qualifications - Experience as an infrastructure, systems, or distributed systems engineer in large-scale or high-availability environments - Strong knowledge of Kubernetes internals, cluster scaling patterns, and containerized workloads - Proficiency in cloud infrastructure concepts (compute, networking, storage, security) and in automating cluster or data center operations Bonus: background with GPU workloads, firmware management, or high-performance computing About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies

About the Team OpenAI’s Inference team powers the deployment of our most advanced models - including our GPT models, 4o Image Generation, and Whisper - across a variety of platforms. Our work ensures these models are available, performant, and scalable in production, and we partner closely with Research to bring the next generation of models into the world. We're a small, fast-moving team of engineers focused on delivering a world-class developer experience while pushing the boundaries of what AI can do. We’re expanding into multimodal inference, building the infrastructure needed to serve models that handle image, audio, and other non-text modalities. These workloads are inherently more heterogeneous and experimental, involving diverse model sizes and interactions, more complex input/output formats, and tighter coordination with product and research. About the Role We’re looking for a software engineer to help us serve OpenAI’s multimodal models at scale. You’ll be part of a small team responsible for building reliable, high-performance infrastructure for serving real-time audio, image, and other MM workloads in production. This work is inherently cross-functional: you’ll collaborate directly with researchers training these models and with product teams defining new modalities of interaction. You'll build and optimize the systems that let users generate speech, understand images, and interact with models in ways far beyond text. In this role, you will: - Design and implement inference infrastructure for large-scale multimodal models. - Optimize systems for high-throughput, low-latency delivery of image and audio inputs and outputs. - Enable experimental research workflows to transition into reliable production services. - Collaborate closely with researchers, infra teams, and product engineers to deploy state-of-the-art capabilities. - Contribute to system-level improvements including GPU utilization, tensor parallelism, and hardware abstraction layers. You might thrive in this role if you: - Have experience building and scaling inference systems for LLMs or multimodal models. - Have worked with GPU-based ML workloads and understand the performance dynamics of large models, especially with complex data like images or audio. - Enjoy experimental, fast-evolving work and collaborating closely with research. - Are comfortable dealing with systems that span networking, distributed compute, and high-throughput data handling. - Have familiarity with inference tooling like vLLM, TensorRT-LLM, or custom model parallel systems. - Own problems end-to-end and are excited to operate in ambiguous, fast-moving spaces. Nice to Have: - Experience working with image generation or audio synthesis models in production. - Exposure to distributed ML training or system-efficient model design. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the

About the Team OpenAI’s Hardware organization develops AI-native silicon and system-level solutions for the unique demands of advanced AI workloads. Building on efforts like Jalapeño, the team is developing future generations of AI-native silicon and tightly integrated systems to power the next generation of frontier models. By co-designing chips, systems, tools, and methodologies, the team helps deliver faster, more efficient, and production-ready hardware for OpenAI’s supercomputing platform. About the Role We are looking for a systems-minded engineer to help advance our kernel development, performance engineering, and hardware-software co-design capabilities, with a particular focus on AI-assisted workflows and tooling. This person will work at the intersection of kernel optimization, developer tooling, observability, and research infrastructure, helping us improve both how production kernels are built and optimized, and how future hardware-software systems are designed and evaluated. The role is ideal for someone who is excited by low-level performance work, but also sees AI and automation as powerful tools for accelerating engineering velocity. You will help define the future of kernel engineering in the era of AI-assisted development. In this role, you may: - Build developer tooling and workflows that make kernel development and performance optimization faster, more scalable, and easier to debug, integrate, and deploy. - Develop observability, diagnostics, and validation infrastructure that makes AI-assisted optimization systems more interpretable, reliable, and effective. - Optimize production kernels end to end by formulating optimization problems, running search loops, analyzing bottlenecks, debugging generated implementations, and landing improvements into production. - Design abstractions, interfaces, and automation systems that accelerate kernel optimization, correctness validation, and hardware-software co-design. - Improve AI-assisted optimization systems for specialized tasks through better datasets, evaluations, benchmarking, and research infrastructure. - Partner across research and engineering teams to turn new ideas into practical systems spanning production needs and long-term infrastructure strategy. You might thrive in this role if you have: - Strong systems or tooling engineering experience, with a background in low-level software, performance optimization, or infrastructure. - Experience with developer tooling, debugging infrastructure, profiling, observability, or workflow design for technical users. - Depth in kernel development, accelerator architecture, compiler systems, or related performance-critical domains. - Familiarity with AI-assisted systems, agentic workflows, post-training, or reinforcement learning for engineering or research applications. - Strong experimental judgment, comfort with ambiguity, and the ability to move fluidly between research exploration and production execution. - Interest in compilers, DSLs, program synthesis, or AI for systems. Preferred profile The ideal candidate is a strong systems and tooling engineer with real depth in kernels and accelerators. They are comfortable working across software and hardware boundaries, can reason deeply about performance, abstractions, and system design, and have hands-on experience optimizing code for GPUs, high-performance CPUs, or custom accelerators. They view AI not as the end product, but as a force multiplier for engineering productivity and system optimization. To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powe

ABOUT THE TEAM We’re hiring software engineers to make OpenAI’s Model Performance teams more productive. These teams work on the systems, tooling, and infrastructure that help improve model performance across OpenAI’s training and inference workloads at frontier scale. ABOUT THE ROLE We’re looking for an autonomous, high-ownership developer productivity engineer who cares deeply about helping other engineers move faster, safer, and with more confidence. This role will sit within OpenAI’s Model Performance organization, contributing to developer infrastructure, CI systems, testing workflows, tooling, and broader performance infrastructure efforts. There is also a strong opportunity to contribute to the Triton project and help improve the systems that support performance-critical engineering work across OpenAI. In this role you will: - Improve development workflows for engineers working on model performance infrastructure - Design and improve CI/CD, release, validation, and testing pipelines - Build and maintain tools that improve reliability, iteration speed, and engineering confidence - Partner closely with engineers to identify friction in testing, debugging, deployment, and development workflows - Contribute to infrastructure efforts that support performance-critical training and inference systems - Help improve developer experience across Python-heavy codebases and performance-oriented infrastructure - Work in a high-context, ambiguous environment where ownership and good judgment matter You might thrive in this role if: - You are motivated by enabling the people around you and helping engineers do their best work - You have strong experience with CI/CD, developer infrastructure, testing systems, tooling, or build/release workflows - You are highly collaborative, empathetic, and comfortable partnering deeply with technical teams - You are strong in Python and enjoy building reliable, scalable developer tools and infrastructure - You have experience improving large-scale engineering workflows, especially around CI reliability, test infrastructure, and debugging velocity - You are self-directed and comfortable operating with ambiguity - You do not need direct inference or model performance experience, but you are excited to learn the domain and make the team meaningfully more effective - Experience in the PyTorch ecosystem is highly relevant - Experience with C++ or Rust is a nice-to-have, but not required - When you see repeated friction — slow tests, flaky CI, brittle release processes, painful debugging, unclear validation — your instinct is to fix the underlying system - You are pragmatic and know how to balance high standards with forward progress About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fai

ABOUT THE TEAM OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. A key part of achieving that mission is training models that deeply understand and reflect human preferences — the Human Data team is at the heart of that effort. The Human Data engineering team creates the systems that enable scalable, high-quality human feedback. These systems are essential to how OpenAI trains and improves its most advanced models. Engineers on this team collaborate closely with world-class researchers to bring alignment techniques to life — from experimental ideas to production-ready feedback loops. ABOUT THE ROLE We’re looking for software engineers to join the Human Data team and build the platforms, prototypes, tools, and infrastructure that power how our AI models are trained, aligned, and evaluated. You’ll partner with researchers and cross-functional teams to bring alignment ideas to life, influence future model training, and shape how models interact with the real world. We’re looking for people who are excited by technical ownership, enjoy working across the stack, and are eager to solve ambiguous problems in a high-impact, fast-paced environment. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. IN THIS ROLE, YOU WILL: - Build and maintain robust full-stack systems for feedback collection, data labeling, and evaluation pipelines, while maintaining high levels of security. - Translate experimental alignment research into scalable production infrastructure, including inference and model training stacks. - Design and iterate on user-facing tools and backend services to support high-quality data workflows - Partner with researchers, engineers, and program leads to shape feedback loops and model interaction paradigms - Drive infrastructure improvements that enable faster iteration and scaling across OpenAI’s frontier models, from internal research tooling all the way to production ChatGPT. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have strong software engineering fundamentals and experience building production systems at scale - Enjoy full-stack development with end-to-end ownership — from backend pipelines to user interfaces - Are motivated by high-impact collaboration with research teams and solving novel, ambiguous problems - Are excited to shape how AI systems learn from human preferences and reflect a broad range of human values - Care deeply about inclusive tooling and building systems that enhance model safety, reliability, and usefulness About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County

About the Team The Fleet team builds core components to enable productive research from small to state of the art scale across OpenAI, with the goal of accelerating progress towards AGI. We frequently collaborate with other teams to speed up the development of new state-of-the-art capabilities. About the Role As we scale up with more researchers and engineers joining OpenAI, we seek a pragmatic and passionate engineer with a strong focus on the development experience for both engineers and scientists. In this role, you will be responsible for building and maintaining systems that allow our research + engineering organization to iteratively develop, test, and deploy new features reliably, with high velocity, and with a frictionless and fast development cycle. You will help oversee and drive to the vision of how we should build, test and deploy software. You will drive the design of our continuous integration pipelines, testing infrastructure, training and support around our build system. Our current environment relies heavily on Python, Rust, and C++, which you will take ownership of and strive to transform into a state of the art development experience for research. Ultimately, your role will be to provide the necessary tools and metrics to support our fast-paced culture and ensure a stable, scalable platform for growth, while also fostering a seamless and low friction experience for OpenAI’s research. This role is based in San Francisco, CA. For a San Francisco role, we use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. You might thrive in this role if you: - Have supported large monorepo development and deployment before - Are a proficient Python programmer working in large monorepos - Are proficient with Docker and Kubernetes - Experienced in CI/CD About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=5

About the Team We believe that increasing compute is a huge lever to AI progress. The Hardware team owns the design and/or sourcing of the compute, storage and interconnect hardware needed to build OpenAI’s supercomputers at the scale needed to deliver AGI that is beneficial to humanity. This includes: - Optimizing the processing hardware for AI models - Designing memory systems to keep up with the needs of training and inference - Enabling the scale-up and scale-out interconnect fabrics create the world’s most power supercomputers We work at the very cutting edge of speed and scale. You won’t encounter another organization with as much compute per employee. We are a small team that moves quickly, with access to huge resources, working with a direct impact on the success of OpenAI and, by extension, the field of AI as a whole. About the Role As a Hardware Chips Programs Manager at OpenAI, you will help bring our chips hardware roadmap to life, navigating an array of technical and partnership challenges. We’re looking for people excited to push the frontiers of computing by navigating technical explorations and are passionate about building. This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. "To comply with U.S. export control laws and regulations, candidates for this role may need to meet certain legal status requirements as provided in those laws and regulations." In this role, you will: - Manage the design and implementation planning of our ML acceleration hardware, working across technical, cross-functional and external stakeholders - Lead planning and scheduling of chip hardware designs with our strategic partners and vendors - Coordinate and marshal internal resources and communication for efficient interaction with partners and vendors. You might thrive in this role if you: - Have experience as a technical program manager for data center hardware products (server, GPU, TPU, networking, storage and so on) - Know the whole end-to-end system program management from concept, design, production, deployment into the data center - Have some experience with System SW programs through NPI - Want to help design some of the world’s largest supercomputing systems, working at the edge of complex hardware challenges - Enjoy working with and enabling world-class AI Researchers and Engineers - Are passionate about the technical program function, and enjoy independently owning and delivering on your teams’ goals and cutting-edge problems in AI compute About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf. Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that crim

ABOUT THE TEAM Preparedness is a critical Safety Research team at OpenAI, which is focused on mitigating AI threats https://openai.com/index/updating-our-preparedness-framework/ that could scale to an extreme level of severity. Our work involves: - Tracking and prediction. Monitoring https://openai.com/index/how-we-monitor-internal-coding-agents-misalignment/ and predicting the evolving misalignment propensities and capabilities of frontier AI systems. - Mitigation. Keeping misuse safeguards, alignment tools, and security measures on track to adequately address extreme threats that might arise in the future. - Coordination. Setting mitigation targets by maintaining OpenAI’s preparedness framework, and partnering with other staff to achieve these targets. This is urgent, fast-paced work that has far-reaching implications for the company and for society. ABOUT THE ROLE As a threat modeler, you will own OpenAI’s holistic approach to identifying, modeling, and forecasting frontier risks from frontier AI systems. This role ensures that our evaluation frameworks, safeguards, and taxonomies are robust, high-coverage, and forward-looking. You will help the company answer the “why” behind our most stringent risk-prevention efforts, shaping the rationale for prioritizing and mitigating risks across domains. You will serve as a central node connecting technical, governance, and policy perspectives on prioritization, focus and rationale on our approach to frontier risks from AI. IN THIS ROLE, YOU WILL: - Develop and maintain comprehensive threat models across all misuse areas (bio, cyber, attack planning, etc.). - Develop plausible and convincing threat models across loss of control, self-improvement, and other possible alignment risks from frontier AI systems - Forecast risks by combining technical foresight, adversarial simulation, and emerging trends. - Pair closely with technical partners on capability evaluations to ensure these map to and cover the gambit of severe risks differentially enabled by frontier AI systems. - Pair closely with Bio and Cyber Leads to size the remaining risk of the designed safeguards and translate threat models into actionable mitigation designs. - Act as the thought partner and explainer of “why” and “when” for high-investment mitigation efforts—helping stakeholders understand the rationale behind prioritization. - Serve as the central node connecting technical, governance, and policy perspectives on prioritization, focus and rationale on our approach to misuse risk. YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Understand risks from frontier AI systems and have a strong grasp of AI alignment literature. Bring deep experience in threat modeling, risk analysis, or adversarial thinking (e.g., security, national security, or safety). - Know how AI evaluations work and can connect eval results to both capability testing and safeguard sufficiency. - Enjoy working across technical and policy domains to drive rigorous, multidisciplinary risk assessments. - Communicate complex risks clearly and compellingly to both technical and non-technical audiences. - Think in systems and naturally anticipate second-order and cascading risks. About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional informatio

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability. You will: - Own large new areas within our product, driving features from design through production deployment. - Work across backend, frontend, and interacting with LLMs and ML models - Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components. - Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences. - Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers. - Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP. - Adapt quickly—learning new technologies, frameworks, and tools as needed across the stack. Ideally you'd have: - 4+ years of full-time engineering experience, post-graduation - Strong experience in Python or Javascript/Typescript - Experience scaling or shipping products at high-growth startups. - Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome). - Proficiency with SQL and modern API development. - Experience with Kubernetes, containerization, and microservice architectures. - Experience working with at least one major cloud provider (AWS, GCP, or Azure). PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the a

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes: - Create multi-layered guardrails around agents - Optimize data retrieval for agents - Orchestrate fleets of asynchronous agents - Automatically alerts users to deviations in data - Illustrating how an agent reached a decision As a Software Engineer, you will own the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders. You will: Design and implement scalable backend systems for Federal customers using cloud-native AI infrastructure. - Build features for agentic systems including multi-layered guardrails and data retrieval optimization. - Develop data pipelines and machine learning infrastructure to make data sources accessible by agents. - Collaborate with cross-functional teams to execute backend solutions for secure environments. - Participate in customer engagements to understand requirements and deliver technical solutions. - Define requirements with stakeholders and implement features until they are accepted. - Contribute to the platform roadmap and product strategy for the Federal business. Ideally you will have: - Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases - Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus - Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles - AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial. - Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles - Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment - Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering <div

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Apply using this link . Applications for the next cohort of Anthropic Fellows close at 11:59pm PT on July 26 . The cohort is expected to start November 2 . In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the November start date doesn't work for you. This page is specific to one of the Anthropic Fellows Workstreams, see also the main Anthropic Fellows posting . Anthropic Fellows Program overview The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent - regardless of previous experience. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis. What to expect - 4 months of full-time research - Direct mentorship from Anthropic researchers - Access to a shared workspace (in either Berkeley, California or London, UK) - Connection to the broader AI safety and security research community - Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country) - Funding for compute (~$15k/month) and other research expenses Interview process The interview process will include an initial application & reference check, technical assessments & interviews, and a research discussion. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Compensation The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (with possible extension). Fellows workstreams Due to the success of the Anthropic Fellows for AI Safety Research progra

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We are looking for biological scientists to help build safety and oversight mechanisms for our AI systems. As a Safeguards Biological Safety Research Scientist, you will apply your technical skills to design and develop our safety systems which detect harmful behaviors and to prevent misuse by sophisticated threat actors. You will be at the forefront of defining what responsible AI safety looks like in the biological domain, working across research, policy, and engineering to translate complex biosecurity concepts into concrete technical safeguards. This is a unique opportunity to shape how frontier AI models handle dual-use biological knowledge—balancing the tremendous potential of AI to accelerate legitimate life sciences research while preventing misuse by sophisticated threat actors. In this role, you will: - Design and execute capability evaluations ("evals") to assess the capabilities of new models - Collaborate closely with internal and external threat modeling experts to develop training data for our safety systems, and with ML engineers to train these safety systems, optimizing for both robustness against adversarial attacks and low false-positive rates for legitimate researchers - Analyze safety system performance in traffic, identifying gaps and proposing improvements - Develop rigorous stress-testing of our safeguards against evolving threats and product surfaces - Partner with Research, Product, and Policy teams to ensure biological safety is embedded throughout the model development lifecycle - Contribute to external communications, including model cards, blog posts, and policy documents related to biological safety - Monitor emerging technologies for their potential to contribute to new risks and new mitigation strategies, and strategically address these Minimum Qualifications: - A PhD in molecular biology, virology, microbiology, biochemistry, systems or computational biology, or a related life sciences field, OR equivalent professional experience - Extensive experience in scientific computing and data analysis, with proficiency in programming (Python preferred) - Deep expertise in modern biology, including both "reading" (e.g. high-throughput measurement, functional assays) and "writing" (gene synthesis, genome editing, strain construction, protein engineering) techniques in biology - Familiarity with dual-use research concerns, select agent regulations, and biosecurity frameworks (e.g., Biological Weapons Convention, Australia Group guidelines) - Strong analytical and writing skills, with the ability to navigate ambiguity and explain complex technical concepts to non-technical stakeholders - Have a passion for learning new skills and an ability to rapidly adapt to changing techniques and technologies - Comfort working in a fast-paced environment where priorities may shift as AI capabilities evolve Preferred Qualifications - Background in AI/ML systems, particularly experience with large language models - Experience in

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Anthropic Anthropic is an AI safety and research company. We build reliable, interpretable, and steerable AI systems, and we believe AI will have a vast impact on the world — our goal is to ensure that impact is positive. About the role Anthropic is compute-constrained, and how we allocate that compute is one of the highest-leverage decisions we make as a company. Today, allocation choices are only loosely tied to the user outcomes we ultimately care about — retention, lifetime value, and the experience of people relying on Claude. This role exists to change that by addressing two intertwined problems at the heart of how we allocate compute. The first is an allocation problem: matching a volatile, heterogeneous stream of demand to a finite, heterogeneous fleet of chips. Which models run on which hardware, in which regions, under what serving configurations — with demand shifting and capacity bounded — is a problem the team navigates continuously today, with more intuition than rigor. You will bring structure to it: building the metrics and analytical frameworks that make the trade-offs legible, and partnering with the infrastructure teams that own these systems to turn that understanding into better decisions. The second is a causal-inference problem: there are many levers — rate limits, pricing, cache behavior, capacity shifts, routing changes — and only a partial picture of what pulling each one actually does to the users on the other end. You will build the causal understanding that closes that gap, choosing whatever approach the question calls for, so allocation decisions are made on expected user impact rather than intuition. This role is a fit for someone who thinks natively in terms of constrained allocation and queueing, who treats "what would happen if we changed X" as an identification problem rather than a dashboard query, and who wants their work to translate into operational and productionized change. You will work closely with the infrastructure engineers who run our compute, and your findings will be presented to senior leadership. Key responsibilities - Build and run testing frameworks — observational and synthetic — to quantify how different inputs affect compute allocation outcomes - Connect compute allocation decisions to downstream user outcomes (retention, lifetime value, revenue) - Partner closely with infrastructure engineers, product, and research to instrument systems, measure what matters, and ship operational changes - Develop the metric hierarchies, dashboards, and reporting that turn supply decisions into shared understanding across the company - Contribute analyses and recommendations to executive forums, and co-author the supply narrative shared with the CTO and staff Minimum qualifications - Strong technical individual-contributor background in data science, analytics, or operations research - Demonstrated comfort reasoning about resource allocation and trade-offs under constraints — drawn to systems problems, not just dashboards - Working fluency with causal inference — able to recogni

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role We're looking for Research Engineers to build the evaluations that tell us — and the world — what Claude can actually do. Your work will turn ambiguous notions of "intelligence" into clear, defensible metrics that researchers, leadership, and the public can rely on. You'll design and implement evaluations across the full spectrum of Claude's capabilities and personality, and build the infrastructure that runs them reliably at scale. You'll partner closely with researchers throughout the lifecycle of a new capability — from defining what to measure, to running the eval against live training checkpoints, to interpreting the results. The goal is to make Anthropic the leader in extremely well-characterized AI systems, with performance that is exhaustively measured and validated across the tasks that matter. Key responsibilities - Design and run new evaluations of Claude's capabilities — reasoning, agentic behavior, knowledge, safety properties — and produce visualizations that make the results legible to researchers and decision-makers - Build and harden the distributed eval execution platform so hundreds of evals run reliably against checkpoints throughout production RL training runs - Own the dashboards researchers and leadership use to monitor model health during training, improving signal-to-noise, reducing latency, and making regressions impossible to miss - Debug anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure issue, and communicate the answer clearly under time pressure - Improve the tooling, libraries, and workflows researchers use to implement and iterate on evaluations - Partner with research teams across the full lifecycle of a new capability — from defining what to measure to interpreting results as training progresses - Run experiments to characterize how prompting, sampling, and scaffolding choices affect results on internal and industry benchmarks - Communicate evaluations and their results to internal stakeholders and, where approp

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team The Universes team within Research is responsible for training AI models to perform complex, difficult, long-horizon agentic tasks in ultra-realistic settings. We design and implement novel training environments that go far beyond what models can do today — environments where models learn to navigate ambiguity, handle interruptions, maintain context over extended interactions, and exercise judgment in open-ended scenarios. About the Role We're looking for Research Engineers to help us build the next generation of training environments for capable and safe agentic AI. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to research direction. You'll work on fundamental research in reinforcement learning, designing training environments and methodologies that push the state of the art, and building evaluations that measure genuine capability. Responsibilities: - Build the next generation of agentic environments - Build rigorous evaluations that measure real capability - Collaborate across research and infrastructure teams to ship environments into production training - Debug and iterate rapidly across research and production ML stacks - Contribute to research culture through technical discussions and collaborative problem-solving You may be a good fit if you: - Are highly impact-driven — you care about outcomes, not activity - Operate with high agency - Have good research taste or senior technical experience, demonstrating good judgment in identifying what actually matters in complex problem spaces - Can balance research exploration with engineering implementation - Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems - Are comfortable with uncertainty and adapt quickly as the landscape shifts - Have strong software engineering skills and can build robust infrastructure - Enjoy pair programming (we love to pair!) Strong candidates may also have one or more of the following: - Have industry experience with large language model training, fine-tuning or evaluation - Have industry experience building RL environments, simulation systems, or large-scale ML infrastructure - Senior experience in a relevant technical field even if transitioning domains - Deep expertise in sandboxing, containerization, VM infrastructure, or distributed systems - &

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team Our team is organized around the north star goal of building an AI scientist—a system capable of solving the long-term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the Role We're seeking a Science Research Operations team member to build and own the operational infrastructure that keeps our research organization running at full speed. Our science teams are working on some of the hardest and most consequential problems in AI—training large-scale models, running complex experiments, and building novel products at the frontier. What makes that possible isn't just talent: it's the coordination, systems, and programs that let researchers spend their time on the science rather than the overhead around it. This role sits at the intersection of research operations, technical program management, and product strategy. You'll work directly with research scientists and research engineers, doing a mix of tasks including running research partnerships, managing complex internal programs, and helping run the team’s day-to-day operations. You'll also contribute to science product development—helping translate research directions into product strategy and ensuring our production deployment environments reflect our best configurations. This is not a pure coordination role. The best candidates will engage substantively with what the team is building, have a role in determining our strategy, spot problems before they surface, and bring genuine ownership to the systems and programs they run. Responsibilities: - Build and manage custom expert contractor networks, sourcing domain specialists for eval and training data work that requires expertise beyond standard channels - Run research partnerships with external partners, from scoping through delivery - Provide end-to-end TPM support for major research pushes—coordinating across teams, tracking dependencies, and keeping stakeholders aligned - Ensure that our research progress is complemented by products that enable scientists to make maximal use of model capabilities. - Support recruiting efforts. - Coordinate external communications for the team, including supporting blog posts and preparing public-facing materials - Partner with product teams to contribute to science product strategy, product design, and novel product integrations where research and product intersect - Own team logistics including onboarding coordination, team events, and operational programs that improve team efficiency You may be a good fit if you: - Have experience in research operations, technical program management, or a related role in a fast-moving technical environment - Can context-switch fluidly between operational work (logistics, tracking, coordination) and higher-order work (strategy, partnerships, product thinking) - Have a technical background, with experience in software development, machine learning, or biology R&D. - Are comfortable working directly with research scientis

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. As a founding member of Life Sciences, you'll work in a high-impact group that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science. Key responsibilities - Design, execute, and iterate on the experimental programs at the core of the team's research: molecular biology, biochemistry, protein and nucleic acid characterization, high-throughput functional screens, and the assay development that makes new questions answerable - Partner directly with computational biologists to design experiments that produce high-quality, analysis-ready data, and feed results back fast enough to immediately inform the next round of analysis - Generate and prioritize hypotheses by combining your experimental judgment with the literature, curated biological knowledge bases, and the team's computational predictions - Use Claude and our internal agent frameworks heavily in your own work — for experimental planning, protocol development, and data interpretation — and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases Minimum qualifications - Have a Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology) or a related field - Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems - Have basic proficiency in Python and are familiar with ML development practices <h2 class="text-text-100 mt-3 -mb-1 text-[1.125rem] font-bold

ABOUT THE ROLE We’re looking for seasoned ML Infrastructure engineers with experience designing, building and maintaining training and serving infrastructure for ML research. Responsibilities: - Provide infrastructure support to our ML research and product - Build tooling to diagnose cluster issues and hardware failures - Monitor deployments, manage experiments, and generally support our research - Maximize GPU allocation and utilization for both serving and training Requirements: - 4+ years of experience supporting the infrastructure within an ML environment - Experience in developing tools used to diagnose ML infrastructure problems and failures - Experience with cloud platforms (e.g., Compute Engine, Kubernetes, Cloud Storage) - Experience working with GPUs Nice to have - Experience with large GPU clusters and high-performance computing/networking - Experience with supporting large language model training - Experience with ML frameworks like Pytorch/TensorFlow/JAX - Experience with GPU kernel development ABOUT CHARACTER.AI Character.AI http://Character.AI empowers people to connect, learn and tell stories through interactive entertainment. Over 20 million people visit Character.AI http://Character.AI every month, using our technology to supercharge their creativity and imagination. Our platform lets users engage with tens of millions of characters, enjoy unlimited conversations, and embark on infinite adventures. In just two years, we achieved unicorn status and were honored as Google Play's AI App of the Year—a testament to our innovative technology and visionary approach. Join us and be a part of establishing this new entertainment paradigm while shaping the future of Consumer AI! At Character, we value diversity and welcome applicants from all backgrounds. As an equal opportunity employer, we firmly uphold a non-discrimination policy based on race, religion, national origin, gender, sexual orientation, age, veteran status, or disability. Your unique perspectives are vital to our success.

ABOUT THE ROLE AND TEAM Joining us as a Research Engineer, you'll be at the forefront of tackling one of the most critical challenges in AI today: safety and alignment. Your work will be pivotal in understanding and mitigating the risks of advanced AI, conducting foundational research to make our models safer, and solving the core technical problems of AI alignment—ensuring our models behave in accordance with human values and intentions. The Safety team is dedicated to pioneering and implementing techniques that make our models more robust, honest, and harmless. As a Research Engineer, you will bridge the gap between theoretical research and practical application, writing high-quality code to test hypotheses and integrating successful safety solutions directly into our products. Your research will not only protect millions of users but also contribute to the broader scientific community's understanding of how to build safe, beneficial AI. WHAT YOU'LL DO - Develop and implement novel evaluation methodologies and metrics to assess the safety and alignment of large language models. - Research and develop cutting-edge techniques for model alignment, value learning, and interpretability. - Conduct adversarial testing to proactively uncover potential vulnerabilities and failure modes in our models. - Analyze and mitigate biases, toxicity, and other harmful behaviors in large language models through techniques like reinforcement learning from human feedback (RLHF) and fine-tuning. - Collaborate with engineering and product teams to translate safety research into practical, scalable solutions and best practices. - Stay abreast of the latest advancements in AI safety research and contribute to the academic community through publications and presentations. WHO YOU ARE - Hold a PhD (or equivalent experience) in a relevant field such as Computer Science, Machine Learning, or a related discipline. - Write clear and clean production-facing and training code - Experience working with GPUs (training, serving, debugging) - Experience with data pipelines and data infrastructure - Strong understanding of modern machine learning techniques, particularly transformers and reinforcement learning, with a focus on their safety implications. - Are passionate about the responsible development of AI and dedicated to solving complex safety challenges. NICE TO HAVE - Experience with product experimentation and A/B testing - Experience training large models in a distributed setting - Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud) - Experience with explainable AI (XAI) and interpretability techniques. - Have research in AI safety, alignment, ethics, or a related area. - Knowledge of the broader societal and ethical implications of AI, including policy and governance. - Publications in relevant academic journals or conferences in the field of machine learning ABOUT CHARACTER.AI Character.AI http://Character.AI empowers people to connect, learn and tell stories through interactive entertainment. Over 20 million people visit Character.AI http://Character.AI every month, using our technology to supercharge their creativity and imagination. Our platform lets users engage with tens of millions of characters, enjoy unlimited conversations, and embark on infinite adventures. In just two years, we achieved unicorn status and were honored as Google Play's AI App of the Year—a testament to our innovative technology and visionary approach. Join us and be a part of establishing this new entertainment paradigm while shaping the future of Consumer AI! At Character, we value diversity and welcome applicants from all backgrounds. As an equal opportunity employer, we firmly uphold a non-discrimination policy based on race, religion, national origin, gender, sexual orientation, age, veteran status, or disability. Your unique perspectives are vital to our success.

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role As a Product Manager focused on Developer Productivity, you'll partner with Infrastructure, Inference, Research, and Product Engineering to build the systems that determine how thousands of engineers and researchers at Anthropic develop, build, test, and ship code—the foundation on which every model, evaluation, and product feature depends: - Partner with Developer Productivity engineering teams to own the end-to-end developer experience—from the source control and language ecosystems that underpin our monorepo, to the build and CI infrastructure that keeps thousands of daily builds running reliably across multiple cloud providers, to the acceleration tooling that deeply integrates Claude into every engineer's workflow. - Your work directly impacts engineering velocity across the entire company: defining the abstractions for how code moves from idea to production, establishing the metrics that surface friction before it compounds, and making the trade-offs that keep a rapidly scaling engineering organization shipping with confidence. - You'll drive the evolution of our developer platform through a fundamental shift in how software gets built—as AI agents move from autocomplete to autonomous collaborators, the definition of "developer" is changing, and our tooling, governance, and workflows must change with it. You'll be defining what developer productivity means when a meaningful share of code is written, tested, and reviewed by Claude itself. - You will define and own the strategy and roadmap across build systems, CI/CD pipelines, developer environments, accelerator toolchain management (GPU, TPU, Trainium), and the AI-native acceleration layer that makes Anthropic the most productive place in the world to build frontier AI. Responsibilities: - Deeply understand the needs of internal customers across Research, Inference, Infrastructure, and Product—from researchers iterating on training code who need fast, reproducible builds to inference engineers managing compute-intensive toolchains with strict compatibility constraints. - Define and iterate on the developer experience model: the workflows, tooling primitives, and feedback loops that govern how engineers and AI agents collaborate on code—including how we measure productivity when the unit of work is no longer a human typing. - Partner with engineering leads to design build, CI, and test infrastructure that scales non-linearly with engineering headcount—ensuring that as Claude takes on more of the inner loop, the outer loop (review, validation, deployment) doesn't become the new bottleneck. - Drive product strategy and roadmap for developer acceleration, including AI-assisted code review, agent-driven test generation, automated dependency management, and the governance frameworks that let teams safely delegate work to autonomous systems. - Own the trade-off framework between velocity, reliability, security, and cost—making transparent prioritization decisions about where to invest in human workflows versus agent workflows, and communicating them clearly to senior leadership. - Establish and champion the productivity metrics that matter in an AI-native engineering org—moving beyond commits an

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Safeguards team is seeking a Red Team Engineer to help ensure the safety of our deployed AI systems and products. In this role, you'll take an adversarial approach to uncover vulnerabilities across our product ecosystem before they can be exploited by malicious actors. Your work will span from technical infrastructure vulnerabilities on our products to emergent risks from advanced AI capabilities. While you'll bring best practices from traditional security approaches, the focus is on broader safety implications and novel abuse unique to advanced AI systems and associated products. You'll investigate the full spectrum of potential abuse — from coordinated account manipulation and payment fraud to novel exploitation of product features — and simulate sophisticated threat actors who chain multiple attack vectors to achieve their objectives. Key responsibilities - Conduct comprehensive adversarial testing across Anthropic's product surfaces, developing creative attack scenarios that combine multiple exploitation techniques - Research and implement novel testing approaches for emerging capabilities, including agent systems, tool use, and new interaction paradigms - Design and execute "full kill chain" attacks that emulate real-world threat actors attempting to achieve specific malicious objectives - Build and maintain systematic testing methodologies that evaluate every aspect of our systems - Develop automated testing frameworks to enable continuous assessment at scale - Collaborate with Product, Engineering, and Policy teams to translate findings into concrete improvements - Help establish metrics for measuring detection effectiveness of novel abuse Minimum qualifications - Experience in penetration testing, red teaming, or application security - Experience in model jailbreaking and testing large-scale agentic workflows for non-obvious prompt injection vectors - Strong technical skills in web application security, including hands-on expertise with security testing tools (e.g., Burp Suite, Metasploit, custom scripting frameworks) - Experience building custom automation, including LLM-specific testing frameworks - A track record of discovering novel attack vectors and chaining vulnerabilities in creative ways - A public body of work such as CVEs, blog posts, or disclosed bug bounty reports - Strong written and verbal communication skills, with the ability to explain technical concepts to varied audiences Preferred qualifications - Experience with AI/ML security or adversarial machine learning - Understanding of AI safety considerations beyond traditional security, including modern guardrails against jailbreaks - Experience testing API security and rate-limiting systems - Background in testing business logic vulnerabilities and authorization bypass techniques - Background in anti-fraud, trust & safety, or abuse prevention syste

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the RL teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Fable 5 and Opus 4.8. Our work spans several key areas: - Developing systems that enable models to use computers effectively - Advancing code generation through reinforcement learning - Pioneering fundamental RL research for large language models - Building scalable RL infrastructure and training methodologies - Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the role We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to design silicon. Hardware design is difficult and unforgiving – exactly the sort of domain we want Claude to excel at. You'll leverage your chip design expertise and turn it into tasks and signals for models to learn from. Specifically, you will: - Invent, design, and implement RL environments and evaluations for agentic RTL generation, design (including formal) verification, physical design optimization. - Work on cross-cutting RL considerations such as EDA-tool latency optimization and proxy rewards. - Conduct experiments and shape our roadmap. - Deliver your work into research and production training runs. - Collaborate with other researchers and engineers across and outside Anthropic. You may be a good fit if you: - Have expertise in ASIC or FPGA design: RTL, design verification (UVM, formal methods, coverage-driven), physical design (synthesis, place-and-route, timing closure), PPA optimization, DFT, ECOs. - Are fluent with industry EDA tools and processes. - Have taped out chips and have experience going from spec to silicon. - Know how to balance research exploration with engineering implementation. - Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: - Experience with reinforcement learning, evaluations or environments. - Built tooling or automation around chip design flows. - Worked on ML accelerators or high-performance compute hardware. - Familiarity with high-level synthesis or architecture simulators. </ul&

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Domain Scaling team has the goal to make Claude world-class at real-world knowledge work in domains like finance, healthcare, and legal. This is a unique role that combines executing directly on applied research and data sourcing (real-world and synthetic) to improve our models. You'll own the end-to-end process of creating RL environments for new capabilities: identifying high-value tasks, designing reward signals, managing vendor relationships, and measuring impact on model performance. Responsibilities - Own the data strategy for knowledge work verticals end-to-end, from task sourcing through RL training - Manage technical relationships with external data vendors, including evaluation of data quality and reward design - Collaborate with domain experts to design data pipelines and evaluations - Explore novel ways of creating RL envs for high value tasks - Develop and improve QA frameworks to catch reward hacking and ensure env quality - Run generalization experiments to measure how data strategy changes improve model capabilities - Partner with other RL research teams and product teams to translate capability goals into training envs and evals You may be a good fit if you - Have experience with fine-tuning large language models for specific domains or real-world use cases - Have experience with reinforcement learning, reward design, or training data curation for LLMs - Are comfortable managing technical vendor relationships and iterating quickly on feedback - Find value in reading through datasets to understand them and spot issues - Have strong cross-functional collaboration skills - Are passionate about making AI more useful and accessible across different industries - Are excited about a role that includes a combination of applied research and hands-on data work Strong candidates may also - Have experience training production ML systems - Have experience designing evals or benchmarks for LLMs - Have domain expertise in a vertical where we would like to make our models more useful - Have experience working with external vendors or technical partners The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: <div class

Scale AI is at the forefront of the AI revolution, helping the U.S. government unlock the potential of AI across national security missions. We’re building enterprise-grade generative AI solutions and delivering them into operational use cases that matter. We’re hiring an engagement manager (EM) to lead and coordinate delivery of agentic workflows for a national security customer. As an EM on our public sector delivery team, you will own or support a large account plan, manage day-to-day execution for customers, and ensure an incredible customer experience. This role is ideal for someone who blends program leadership, customer relationship building, technical fluency, and contract awareness — and who thrives in fast-moving, ambiguous, and mission-driven environments. You will: - Manage customer relationships from the executive to the end user - Work alongside customers to scope agentic workflow use cases that Scale’s engineering team will build and you will ultimately refine - Lead or support a cross-functional project team to deliver on and exceed the customer's AI/ML objectives - Lead with a “whatever-it-takes” mentality, proactively identifying customer needs and operator pain points to ensure customer success - Oversee onboarding and successful implementation of customer accounts We have a diverse team with a variety of skill sets, many have: - A technical background (education or professional experience with computer science, economics, statistics, engineering) - A proven track record in B2B client-facing roles and expanding client relationships - Prior experience owning the technical implementation of solutions to the government Must haves: - An active TS/SCI clearance (Full Scope Polygraph is a nice to have) - 3+ years of work experience succeeding in stakeholder management or a customer-facing role delivering enterprise-scale applications / solutions - A track record of structured, analytics-driven problem solving - Excellent verbal and written communication skills - Ability to be forward deployed and willingness to travel 25% (50% travel is a nice to have) Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. The base salary range for this full-time position in the location o

The Enterprise Account Executive (Financial Services) will report to the Director of Enterprise GTM and own revenue growth across a portfolio of Scale AI’s most strategic financial services customers and prospects. This role is focused on selling complex, agentic AI solutions -autonomous workflows powered by LLMs and human-in-the-loop systems - into large banks, insurers, asset managers, and fintechs. You will operate as a strategic partner to senior executives across the business, technology, and risk organizations - helping them reimagine core workflows (e.g., underwriting, fraud detection, KYC, claims, research, and operations) through AI agents. This is a highly consultative, technical enterprise sales role requiring deep domain fluency, executive presence, and the ability to navigate regulatory, security, and multi-stakeholder complexity. You will own the full customer lifecycle - from origination through close, deployment, and expansion - while acting as the quarterback across Solutions Engineering, Product, Research, and Delivery teams to land and scale high-impact AI programs. You Will: - Own and expand relationships with the largest financial services institutions (banks, insurers, capital markets, fintech), focusing on high-impact, multi-year AI transformations - Sell agentic AI solutions by mapping Scale’s capabilities to mission-critical workflows (e.g., underwriting, fraud, compliance, customer ops, investment research) - Build trusted relationships with executive stakeholders (CIO, CTO, Chief Data/AI Officer, Heads of Risk/Operations/Lines of Business) and guide enterprise AI strategy - Develop and execute multi-threaded account plans that drive net-new revenue, expansion, and long-term platform adoption - Lead complex deal cycles, including business case development, ROI modeling, and mutual close plans across new business, renewals, and expansions - Partner deeply with Solutions Engineering to shape and land technically credible pilots, POVs, and production deployments - Navigate regulatory, security, and procurement processes unique to financial services environments - Act as the voice of the customer internally—informing product roadmap, agent design, and vertical-specific solutions - Maintain a strong command of pipeline, forecasting, and deal hygiene using Salesforce, Clari, and related tools - Operate with urgency and precision in a fast-paced, highly cross-functional environment Ideally, You Will Have: <li data-section-id="1idtofq" data-start="2684" data-end

The Enterprise Account Executive (Healthcare & Life Sciences) will report to the Director of Enterprise GTM and own revenue growth across a portfolio of Scale AI’s most strategic healthcare and life sciences customers. This role is focused on selling complex, agentic AI solutions - autonomous workflows powered by LLMs and human-in-the-loop systems - into health systems, payers, pharma, biotech, and digital health organizations. You will act as a strategic partner to clinical, operational, and technical leaders - helping them transform core workflows such as clinical documentation, prior authorization, revenue cycle, pharmacovigilance, clinical trials, medical affairs, and patient engagement through AI agents. This is a highly consultative, technical enterprise sales role requiring deep domain fluency, strong executive presence, and the ability to navigate regulatory, compliance, and multi-stakeholder complexity. You will own the full customer lifecycle - from origination through close, deployment, and expansion - while quarterbacking cross-functional teams across Solutions Engineering, Product, Research, and Delivery to land and scale high-impact AI programs. You Will: - Own and expand relationships with leading healthcare and life sciences organizations (providers, payers, pharma, biotech), focusing on multi-year, strategic AI initiatives - Sell agentic AI solutions by mapping Scale’s capabilities to high-impact workflows (e.g., clinical documentation, prior auth, revenue cycle, patient ops, clinical trials, drug safety, medical review) - Build trusted relationships with executive stakeholders (CIO, CTO, CMIO, Chief Data/AI Officer, Heads of Clinical Ops, R&D, Commercial) - Develop and execute multi-threaded account strategies that drive net-new revenue, expansion, and long-term platform adoption - Lead complex deal cycles, including ROI modeling, business case development, and mutual close plans across new business, renewals, and expansions - Partner closely with Solutions Engineering to design and land technically credible pilots, POVs, and production deployments - Navigate healthcare-specific regulatory and compliance requirements (e.g., HIPAA, GxP, data governance, auditability) throughout the sales process - Act as the voice of the customer internally - informing product roadmap, agent design, and vertical-specific solutions - Maintain strong pipeline discipline, forecasting accuracy, and deal hygiene using Salesforce, Clari, and related tools - Operate effectively in a fast-paced, cross-functional environment with high ownership and attention to detail Ideally, You Will Have:</strong&g

Scale is at the forefront of the AI revolution, working with some of the largest companies in the world to unlock the potential of Generative AI for their business. We develop bespoke solutions that leverage our customer’s proprietary data and expertise to transform their businesses with AI. We work with them to understand the biggest levers for their business and then forward deploy with their teams to build cutting edge solutions. The applications we build are powered by the Scale GenAI Platform, a full stack product to build, test and deploy cutting edge agents. Some examples of GenAI applications we build are: - Content-generation systems that enable sales teams to be more effective and efficient. - Highly customized wealth management copilots that make advisors more effective by helping them tap into their knowledge bases quickly and accurately. - Text2SQL business intelligence applications to make analysts more efficient and embed a culture of data-driven decision-making. We are seeking an experienced product manager to join our team and play a pivotal role in building AI solutions with and for our customers. The ideal candidate will have a strong understanding of software engineering principles and practices and deep experience with ML/AI application development. You will be responsible for owning large AI projects for one or many customers. You will: - Develop enterprise grade solutions that leverage cutting edge AI to drive business value at world class companies across many industries. - Work with executives at Scale and our customers to determine and execute the product strategy of the business. - Own end-to-end product development by understanding customer pain points, defining product requirements, managing development, testing, and launches. - Lead cross-functional teams including engineering, product design, operations, marketing, go-to-market and finance. - Develop a point of view and execute on turning the solutions we build into repeatable software that we can commercialize across the industry. Ideally you’d have: - Technical degree in computer science, engineering, or equivalent experience - 4+ years of experience in building ML-powered products, experience in enterprise-facing products is a plus - Strong understanding of generative AI technologies and their applications in enterprise settings - Experience operating in a fast-paced environment with high ambiguity - Exceptional leadership, presentation and communication skills with the ability to influence cross-functional teams - Some coding experience (Python) PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the A

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, governments, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences. About the Role We're looking for a Partnerships Manager to drive forward the clinical AI research agenda for Anthropic's global health work — informing the tools that bring Claude safely into care and generating the evidence that proves they work. You'll help define how clinical AI tools should be validated for use in low- and middle-income countries (LMICs), and you'll help shape the tools and safeguards that make Claude safe and usable in clinical settings. This is a hands-on role as much as a research one: you'll work side by side with Anthropic's research, evals, and product teams, translating what you know from how care actually gets delivered in low-resource settings into evaluations, safeguards, and product improvements. You'll join a small, tight-knit global health team within Beneficial Deployments. While you'll lead on this domain, you should expect to roll up your sleeves on adjacent workstreams, help shape overall team strategy, and be a thought partner to colleagues working on other parts of the health system. Everyone on the team owns the whole mission, not just their lane. Key responsibilities - Own the clinical research and evaluation agenda for our global health work — define what we need to prove, to what standard, and with whom, and drive it. - Design clinical evaluations and validation frameworks for LLMs in LMIC contexts, covering accuracy, safety, multilingual performance, and real-world conditions, in close partnership with Anthropic's research, evals, and product teams. - Develop theories of change and outcome metrics connecting model capability to care quality, health-worker performance, and patient outcomes. - Build and manage our global research partnerships, and engage with relevant regulatory and normative bodies (WHO, national authorities, research-ethics bodies). - Partner with internal research and product teams to improve Claude for clinical use cases in low-resource settings. Stay grounded in how care is actually delivered in LMICs so our tools and evaluations reflect those realities. - Contribute across the broader global health portfolio — lean in on adjacent workstreams, help set strategy, and be a thought partner to the team. Minimum qualifications - Medical training and clinical practice (MD, GP, MBBS, DO, or equivalent), with direct experience delivering care in low-resource settings — you reason fluidly from how diagnosis, triage, treatment, and referral actually happen at the point of care in low and middle income countries. - A concrete, on-the-ground understanding of clinical and care-delivery workflows in LMIC

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: We are seeking a People Research Scientist to join our People Data Solutions team. You’ll be the research expert supporting our broader People organization, using rigorous scientific methods to advance our understanding of the employee experience, manager effectiveness, organizational health, and workforce dynamics. This role sits at the intersection of organizational science, behavioral research, and people strategy – developing novel frameworks and conducting systematic research that drives evidence-based people decisions across our growing organization. This role offers the opportunity to make a significant impact on both our people practices and the broader field of people science at a leading AI safety company. Responsibilities: Research Design & Scientific Inquiry - Design and execute systematic research studies to answer fundamental questions about employee experience, manager effectiveness, and organizational health - Generate and test hypotheses about people programs, employee behavior, and workforce outcomes using rigorous experimental and quasi-experimental methods - Conduct longitudinal studies tracking employee cohorts to understand long-term workforce trends and the impact of people initiatives - Perform meta-analyses of people interventions across the industry to identify best practices and knowledge gaps - Navigate research ethics considerations when studying employee data, ensuring responsible research practices Employee listening & survey research - Design, analyze, and iterate on employee listening programs including engagement surveys, pulse surveys, and lifecycle surveys - Apply psychometric methods to validate survey instruments and ensure measurement reliability - Translate survey findings into strategic recommendations that drive meaningful organizational change Manager research & organizational effectiveness - Conduct research on manager behaviors, competencies, and their impact on team outcomes - Build measurement frameworks to evaluate and improve manager effectiveness programs - Study organizational dynamics including team composition, collaboration patterns, and their relationship to performance outcomes Visualization & communication - Build compelling visualizations and dashboards that make complex research findings accessible to diverse audiences - Present research findings to senior leadership with clear, actionable recommendations - Develop self-service analytics capabilities that empower People team partners Minimu

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's inference fleet serves Claude to millions of users across our own products and the world's largest cloud platforms. The stack that makes this possible is deep and tightly coupled: accelerator kernels, model servers, distributed routing, autoscaling, capacity management. Every layer affects the others, often in ways that are hard to see in isolation. The Inference System Dynamics team is responsible for understanding that whole system and holding it to a high bar across four dimensions: throughput, latency, reliability, and correctness . We measure how the fleet performs against its theoretical performance frontier, run cross-layer investigations to explain the gaps, and own the correctness checks that make sure Claude's outputs are right, not just fast, across hardware platforms and serving configurations. We don't own the individual components. We instrument and model them, find the highest-leverage opportunities across them, and partner with the owning teams to land the wins. You'll work across all four areas. One week that might mean tracing a tail-latency regression from request timing down through routing and batching into a kernel overhead; the next it might mean tightening a correctness eval so it catches an output regression introduced by a quantization change. We're looking for performance engineers who treat correctness as part of performance. Key Responsibilities - Run cross-layer performance investigations across throughput, latency, and reliability, sizing the gap between actual fleet performance and theoretical rooflines, identifying root causes, and quantifying the value of closing them - Own and improve the correctness evaluation pipeline that validates model output quality across hardware platforms, numerics, and serving configurations, and lead the investigation when it catches a regression - Build the observability, dashboards, and modeling tools that make throughput, latency, cost, reliability, correctness, and their interactions legible across the stack - Partner with kernel, serving, routing, autoscaling, and capacity teams to prioritize and land the highest-impact optimizations your analysis surfaces - Ruthlessly stack-rank a large surface area of opportunities by impact and effort, and say no to the ones that don't make the cut Minimum Qualifications - Hands-on performance engineering experience: profiling, roofline analysis, latency/throughput optimization, and root-cause investigation in complex production systems - Proficiency in Python, with the ability to read, instrument, and contribute to large production codebases you didn’t write - Solid data analysis skills (e.g. SQL, pandas, or similar) sufficient to turn raw telemetry into clear findings - Ability to communicate quantitative results clearly in writing to influence priorities on teams you don't manage - Genuine interest in correctness as an engineering discipline: numerics, evaluation design, regression detection Preferred Qualifications - Expe

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As an Economist at Anthropic, you will work to measure and understand AI's effects on the global economy. You will make fundamental contributions to the development of the Anthropic Economic Index, establishing new methodologies to measure the usage, diffusion, and impact of AI throughout the economy using privacy-preserving tools and novel data sources. You will use frontier methods in econometrics, machine learning, and structural estimation. Such rigour will drive impact, shaping both policy discussions externally and informing Anthropic’s internal business and product decisions. Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system ( Clio ), we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks. Responsibilities - Make fundamental contributions to the development and expansion of the Anthropic Economic Index , including quarterly reports and industry-specific deep dives - Design and conduct empirical research on AI's economic effects, drawing on external data sources and the privacy-preserving measurement systems internally - Develop new methodological approaches for studying AI's impact on: - Labor markets and the future of work - Productivity and task transformation - Economic inequality and displacement - Industry-specific disruption and adaptation - Aggregate economic trajectories (GDP, productivity, unemployment) under varying AI-adoption scenarios - Develop causal-inference tooling — e.g. surrogate indexes, heterogeneous-effect pipelines — to help Anthropic evaluate the downstream economic consequences of its own compute, product, and pricing decisions - Build and maintain relationships with academic institutions, policy think tanks, and other research partners - Work cross-functionally with other technical teams to improve our measurement infrastructure and data collection - Translate research insights into actionable recommendations for both product decisions and policy discussions - Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders You May Be a Good Fit If You Have - PhD in Economics - Strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning - Experience relevant to the study of AI’s impact on the economy, including: - Labor market analysis and occupational change - Task-based appr

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Team The Frontier Red Team (FRT) is a small, focused technical research team within Anthropic's Policy organization. Our goal is to make the entire world safer in an era of advanced AI by understanding what these systems can do and building the defenses that matter. In 2026, we're focused on researching and ensuring safety with self-improving, highly autonomous AI systems, especially ones related to cyberphysical capabilities. See our previous related work on exploits , partnering with Mozilla , and zero days . This is early-stage, high-conviction research with the potential for outsized impact — Glasswing is one example. Note: We are exclusively hiring in SF. We support relocation, but all hires must relocate before starting. About the Role In the last year, we've seen compelling signs that LLMs and agents are increasingly capable of novel cyber capabilities. We think 2026 will be the year where models reach expert-level, even superhuman, in several cybersecurity domains. This is a novel and massive threat surface. As a Research Scientist on FRT focusing on cyber, you'll build the tools and frameworks needed to defend the world against advanced AI-enabled cyber threats. Senior candidates will have the opportunity to shape and grow Anthropic's cyberdefense research program, working with Security, Safeguards, Policy, and other partner teams. This work sits at the intersection of AI capabilities research, cybersecurity, and policy—what we learn directly shapes how Anthropic and the world prepare for AI-enabled cyber threats. This is applied research with real-world stakes. Your work will inform decisions at the highest levels of the company, contribute to demonstrations that shape policy discourse, and build the technical defenses that we will need for a future of increasingly powerful AI systems. What You'll Do - Develop systems, tools, and frameworks for AI-empowered cybersecurity, such as autonomous vulnerability discovery and remediation, malware detection and management, network hardening, and pentesting - Design and run experiments to elicit and evaluate autonomous AI cyber capabilities in realistic scenarios - Design and build infrastructure for evaluating and enabling AI systems to operate in security environments - Translate technical findings into compelling demonstrations and artifacts that inform policymakers and the public - Collaborate with external experts in cybersecurity, national security, and AI safety to scope and validate research directions - Senior candidates will also set research strategy, define what problems are worth solving, own the technical roadmap, and manage relationships with cross-functional partners Sample Projects - Building frameworks and tools that enable AI models to autonomo

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About Horizons The Horizons team leads Anthropic's reinforcement learning (RL) research and development, playing a critical role in advancing our AI systems. We've contributed to every Claude release, with significant impact on the autonomy, coding, and reasoning capabilities of Anthropic's models. About the role We're hiring for the Cybersecurity RL team within Horizons. As a Research Engineer, you'll help to safely advance the capabilities of our models in secure coding, vulnerability remediation, and other areas of defensive cybersecurity. This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic. The role requires domain expertise in cybersecurity paired with interest or experience in training safe AI models. For example, you might be a white hat hacker who's curious about how LLMs could augment or transform your work, a security engineer interested in how AI could help harden systems at scale, or a detection and response professional wondering how models could enhance defensive workflows. You may be a good fit if you: - Have experience in cybersecurity research. - Have experience with machine learning. - Have strong software engineering skills. - Can balance research exploration with engineering implementation. - Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: - Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work. - Experience participating in or building CTF competitions and cyber ranges. - Academic research experience in cybersecurity. - Familiarity with RL techniques and environments. - Familiarity with LLM training methodologies. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $300,000 - $405,000 USD <strong&gt

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities - Build and improve the RL training infrastructure that researchers depend on day-to-day - Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed - Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster - Own the reliability and performance of research runs end-to-end - Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you - Have strong software engineering fundamentals and a track record of building performant, reliable systems - Have worked on ML infrastructure, distributed systems, or research tooling - Care about enabling other people's work and find leverage through platforms rather than individual experiments - Are comfortable operating across the stack, from low-level performance work to RL algorithms - Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have - Experience with large-scale distributed training (RL, pre-training, or post-training) - Familiarity with JAX, PyTorch, or similar ML frameworks - A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 - $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: </strong&

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: When you see what modern language models are capable of, do you wonder, "How do these things work? How can we trust them?" The Interpretability team at Anthropic is working to reverse-engineer how trained models work because we believe that a mechanistic understanding is the most robust way to make advanced systems safe. We’re looking for researchers and engineers to join our efforts. People mean many different things by "interpretability". We're focused on mechanistic interpretability, which aims to discover how neural network parameters map to meaningful algorithms. Some useful analogies might be to think of us as trying to do "biology" or "neuroscience" of neural networks using “microscopes” we build, or as treating neural networks as binary computer programs we're trying to "reverse engineer". A few places to learn more about our work and team at a high level are this introduction to Interpretability from our research lead, Chris Olah ; a discussion of our work on the Hard Fork podcast produced by the New York Times, and this blog post (and accompanying video) sharing more about some of the engineering challenges we’d had to solve to get these results. Some of our team's notable publications include A Mathematical Framework for Transformer Circuits , In-context Learning and Induction Heads , Toy Models of Superposition , Scaling Monosemanticity , and our Circuits’ Methods and Biology papers. This work builds on ideas from members' work prior to Anthropic such as the original circuits thread , Multimodal Neurons , <a class="text-accent-seco

At Scale AI, we’re not just building AI tools—we’re pioneering the next era of enterprise AI. As businesses race to harness the power of Generative AI, Scale is at the forefront, delivering cutting-edge solutions that transform workflows, automate complex processes, and drive unparalleled efficiency for the largest enterprises. Our Scale Generative AI Platform (SGP) provides foundational services and APIs, enabling businesses to seamlessly integrate AI into their operations at production scale. We’re looking for a Backend Engineer to help bring large-scale GenAI systems to production. In this role, you’ll build the core infrastructure that powers AI products for some of the world’s largest enterprises—designing scalable APIs, distributed data systems, and robust deployment pipelines that enable production-grade reliability and performance. This is a rare opportunity to be at the center of the GenAI revolution, solving hard backend and infrastructure challenges that make AI truly work at enterprise scale. If you're excited about shaping how AI systems are deployed and scaled in the real world, we want to hear from you. At Scale, we don’t just follow AI advancements — we lead them. Backed by deep expertise in data, infrastructure, and model deployment, we are uniquely positioned to solve the hardest problems in AI adoption. Join us in shaping the future of enterprise AI, where your work will directly impact how businesses operate, innovate, and grow in the age of GenAI. You Will: - Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure. - Develop core services and APIs that integrate AI models and enterprise data sources securely and efficiently, enabling production-scale AI adoption. - Architect scalable distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads. - Optimize backend performance for latency, throughput, and cost—ensuring AI applications can operate at enterprise scale across hybrid and multi-cloud environments. - Manage and evolve cloud infrastructure (AWS, Azure, or GCP), driving automation, observability, and security for large-scale AI deployments. - Collaborate with ML and product teams to bring cutting-edge GenAI models into production through efficient APIs, model serving systems, and evaluation frameworks. - Continuously improve reliability and scalability , applying strong engineering practices to make AI systems robust, maintainable, and enterprise-ready. Ideally, You Have: - 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability. - Proficiency in Python or TypeScript , with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS. - Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform. - Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive app

At Scale AI, we’re not just building AI tools—we’re pioneering the next era of enterprise AI. As businesses race to harness the power of Generative AI, Scale is at the forefront, delivering cutting-edge solutions that transform workflows, automate complex processes, and drive unparalleled efficiency for the largest enterprises. Our Scale Generative AI Platform (SGP) provides foundational services and APIs, enabling businesses to seamlessly integrate AI into their operations at production scale. We’re looking for a Backend Engineer to help bring large-scale GenAI systems to production. In this role, you’ll build the core infrastructure that powers AI products for some of the world’s largest enterprises—designing scalable APIs, distributed data systems, and robust deployment pipelines that enable production-grade reliability and performance. This is a rare opportunity to be at the center of the GenAI revolution, solving hard backend and infrastructure challenges that make AI truly work at enterprise scale. If you're excited about shaping how AI systems are deployed and scaled in the real world, we want to hear from you. At Scale, we don’t just follow AI advancements — we lead them. Backed by deep expertise in data, infrastructure, and model deployment, we are uniquely positioned to solve the hardest problems in AI adoption. Join us in shaping the future of enterprise AI, where your work will directly impact how businesses operate, innovate, and grow in the age of GenAI. You Will: - Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure. - Develop core services and APIs that integrate AI models and enterprise data sources securely and efficiently, enabling production-scale AI adoption. - Architect scalable distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads. - Optimize backend performance for latency, throughput, and cost—ensuring AI applications can operate at enterprise scale across hybrid and multi-cloud environments. - Manage and evolve cloud infrastructure (AWS, Azure, or GCP), driving automation, observability, and security for large-scale AI deployments. - Collaborate with ML and product teams to bring cutting-edge GenAI models into production through efficient APIs, model serving systems, and evaluation frameworks. - Continuously improve reliability and scalability , applying strong engineering practices to make AI systems robust, maintainable, and enterprise-ready. Ideally, You Have: - 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability. - Proficiency in Python or TypeScript , with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS. - Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform. - Experience managing data systems such as relational and NoSQL databases (PostgreSQL, Dyna

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition. At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI. At the foundation of these products is the Identity Engineering team. In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication. You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies. You will: - Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems. - Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect). - Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access control (RBAC). - Build software-defined identity governance policies to ensure compliance with security policies, industry regulations (e.g., NIST, SOC2, ISO 27001), and organizational standards. - Present technical information to teams and stakeholders, providing guidance and insight on identity management and best practices. Ideally you’d have: - 3+ years of full-time engineering experience, post-graduation with specialities in infrastructure and identity systems. - Infrastructure expertise – IAM controls, Infrastructure as Code (Terraform, Pulumi), microservice deployment best practices. - Hands-on experience working with OpenFGA, Authzed, Cedar, Topaz, or similar authorization frameworks at scale. - Strong understanding of Zanzibar-based ReBAC models, relationship tuples, and access control evaluation. - Strong knowledge of authentication standards such as OAuth 2.0, OIDC, SAML, and JWT, as well as industry standard IdP solutions like EntraID, Okta, etc. - Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred). - Show a track record of independent ownership of successful engineering projects. - Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders. Nice to haves: - Experience securing API access and implementing access control mechanisms at the application level. - Multi-cloud infrastructure experience – AWS, Azure, GCP, and more. &lt

The next frontier for AI is the physical world. At Scale, we're pioneering this shift, moving artificial intelligence from digital spaces into robotics and autonomous systems. Our Autonomous Driving (AD) and Robotics team is building the data engine and infrastructure that powers L3/L4 autonomy and complex robotic manipulation. We are looking for a pivotal Solutions Engineer to join this team.. As a Solutions Engineer, you'll be a trusted technical partner to the world's most innovative Foundation Model builders and renowned robotics companies (from Humanoids to Robotaxis). You will partner closely with Product, Sales, and ML Engineers to guide prospective customers through the pre-sales process, delivering customized demos and Proof of Concepts that secure the "technical win. You’ll help customers bridge the gap between simulation (Digital Twins) and real-world deployment by defining technical requirements for multi-modal real-world data pipelines (LiDAR, Radar, Camera, and IMU). You'll develop actionable Statements of Work and collaborate with the delivery team on high-fidelity ground truth implementation. Your expert knowledge of Scale's products will allow you to design creative, impactful solutions. This is a critical role that directly influences multi-million dollar contracts and initiatives. You'll travel globally to conduct on-site technical workshops and scope new projects, while also leading demos and pilots for new prospects. You'll be part of a tight-knit, specialized team, influencing a rapidly growing business that is expanding into new product areas. In this role, you will: - Partner with Scale Account Executives and Engagement Managers to deliver new customer pilots and grow technical relationships with existing clients. - Work with Product Engineering and Product Management to influence our product roadmap based on your frontline insights. - Become a domain expert in next-generation Robotics and physical AI (e.g. VLMs, VLAs, World Models) - Develop technical domain expertise in areas of 2D and 3D imaging and annotation, multi-sensor fusion and calibration, GPS/INS navigation systems, computer vision and other autonomy-adjacent concepts - Be accountable for the technical customer experience and commercial growth, expanding relationships and use cases with existing customers. - Collaborate with highly technical engineers at our customer sites to ensure satisfaction with our data, software platforms, and workflows. - Design and develop playbooks, demos, and other tools to ensure efficient and successful pilots and customer expansions. - Pioneer the development of a global Robotics Data Marketplace, actively seeking out and engaging with key international partners to build a comprehensive data ecosystem. - Evangelize Scale by interacting with customers at major industry events and academic conferences. You have: - A strong engineering background, preferably in Robotics, Mechatronics, Computer Science, Mathematics, or other Engineering fields. - 3+ years of experience developing with Python, C++, Java, and/or other scripting languages. - Hands-on experience in Autonomous Driving, Robotics or Physical AI - Exceptional project management and interpersonal skills, strong attention to detail, and a strong sense of ownership. - The presentation skills and technical credibility to speak confidently with a variety of stakeholders, from executives to front-line engineers. - A high level of comfort communicating effectively across internal and external

Our customer base is growing exponentially, and you will be on the front lines of ensuring that the world's most innovative companies become passionate, lifelong, Scale customers. Our Solutions Engineers ensure customers' first experiences with Scale's technology are flawless and lead to a successful long-term partnership. The work will vary daily, and we’re looking for technical experts excited to solve tough problems. As a solution engineer, you will be a part of helping shape our early-stage federal business by re-envisioning our commercial product offerings for our federal clients. What you'll do: - Become an expert on the end-to-ends of Scale Products - Create tailored demonstrations and collateral for federal stakeholders at both the executive and analyst level. - Partner with Scale Account Executives to deliver customer pilots according to requirements agreed by the customer. - Integrate and ingest a variety of external datasets to solve government use cases. - Interact with customers on a day-to-day basis to understand their pain points and design solutions - Work with internal product and engineering teams to turn customer requirements into Scale capabilities - Understand public sector mission sets and strategic objectives to better showcase Scales products. Ideally you'd have: - Strong engineering background, preferably in computer science, mathematics, or other quantitative fields - Strong communication skills - ability to interact with both technical and non-technical customers at all levels - At ease with technology, able to quickly pick up new tech stacks and troubleshoot - Previous experience working with Public Sector customers - our business is diverse and growing across both National Security and Federal Civilian communities. - Proficiency in scripting languages such as Python, Javascript/Typescript, Bash scripts, or programming languages. - A strong desire to roll up your sleeves and help build a business in an extremely fast-paced environment - Active US Government Security Clearance (TS / SCI required) - Based in the Washington, DC area or willing to relocate - Background working in AI/ML, particularly Generative AI and Large Language Models Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. </

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, you will improve the capabilities, reliability, and product fit of OpenAI's agentic models. You might own a research direction, build the infrastructure that makes large training runs faster and more trustworthy, create evals that reveal where models fail, or drive a capability from an idea through experimentation, integration, and launch. This role is intentionally broad. The strongest candidates are not defined by one method or subfield; they are people who can take an ambiguous capability problem and make progress across research, engineering, data, evals, and product. You should be excited to work on models that act in the world: writing and debugging code, using tools, calling functions, operating computers, collaborating with other agents, and completing valuable work on behalf of users. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might - Design and run experiments that improve agentic model behavior across coding, tool use, function calling, computer use, multi-agent collaboration, long-horizon tasks, factuality, instruction following, and calibrated reasoning. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex, API/platform, and ChatGPT product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. You might thrive in this role if you - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthe

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a member of this API & power-users team, you will improve the capabilities, reliability, and product fit of OpenAI’s agentic models for power users and API developers. You might design evals from real developer workflows, build training environments around production-like tool use, turn qualitative model failures into training data, evals, or post-training interventions, or drive a behavior improvement from discovery through post-training, integration, and launch. This role is intentionally broad. The strongest candidates are comfortable turning ambiguous model behavior problems into concrete progress, whether that means improving tool use, planning, instruction following, recovery from mistakes, or how models behave in API-based workflows. You should be excited to work across research, engineering, data, evals, and product to make models better at acting in real workflows. You will work closely with researchers, engineers, API/product teams, Codex, infrastructure, and safety/alignment partners to decide which behaviors matter, how to measure them, how to train them, and when they are ready for major model runs. This is a high-agency role for people who want their work to show up directly in frontier models used by expert users and developers. IN THIS ROLE, YOU MIGHT - Design and run experiments that improve model behavior in API and power-user workflows: function calling, tool use, coding, planning, long-horizon execution, factuality, instruction following, error recovery, and calibrated reasoning. - Build evals, graders, and environments from real developer and power-user workflows, then turn observed failures into training data, model-behavior hypotheses, and shipped improvements. - Partner with API and power-users to identify high-leverage behavior gaps and convert product signals into post-training interventions. - Improve how models behave when composed into systems: using tools reliably, respecting developer intent, handling partial failures, asking for clarification when appropriate, and maintaining coherence across multi-step tasks. - Own end-to-end model behavior projects, from qualitative failure analysis through data generation, training experiments, eval design, integration into major runs, and launch readiness. - Develop feedback loops that use power-user traces, API usage patterns, and production-like environments to discover the next frontier of agentic model failures and gaps. - Help decide which agentic capabilities, behavioral fixes, and partner-team integrations are ready for inclusion in major model runs. - Debug hard failures in shipped or near-shipped models by moving between traces, evals, training data, model outputs, and product context. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, c

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role As a member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach our models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This work will require owning improvements across our post-training stack, including RL, data pipelines, graders, reward signals, evals, and behavioral analysis. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you will: - Design and run experiments that improve agentic model behavior for complex software and plugins.. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. You might thrive in this role if you: - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution. - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to wor

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE As a member of Agent Post-Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi-step workflows through the tools teams already use. You will train models to be supercharged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT - Design and run experiments that improve agentic model behavior for complex software and plugins.. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. YOU MIGHT THRIVE IN THIS ROLE IF YOU - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and

ABOUT THE TEAM The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. ABOUT THE ROLE We believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. IN THIS ROLE, YOU MIGHT - Design and run experiments that improve scaling of compute on context. - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions. - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements. - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior. - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs. - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness. - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments. - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes. YOU MIGHT THRIVE IN THIS ROLE IF YOU - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution. - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with. - Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next. - Are comfortable working across research, product, infrastructure, data