Role summary Scale’s Operations Planning Team sits at the intersection of Engagement Management (EM), GenAI Delivery, GTM, and Growth Ops. The Planning TPM reduces friction between customer requirements and delivery by centralizing planning, standardizing reporting, and driving consistent process execution across accounts. As a TPM on Planning you will own program-level planning and systems workstreams that translate demand into reliable production plans, validate staffing, and ensure opportunities move through ingest accurately. This role combines program management, operational rigor, analytics literacy, and strong stakeholder management, with the goal of making account planning predictable, auditable and scalable. This is a role designed to build deep account knowledge: each Planning TPM will ultimately be assigned to support 1–2 priority accounts to develop the context and relationships required to drive lasting operational improvements and more accurate planning and forecasting. What you’ll own (key responsibilities) Demand Forecasting - Ensure project-level customer demand is captured accurately, consistently and at the right level of granularity. - Own day-to-day monitoring and execution of Production Plan and Opportunity Forecast; act quickly on demand changes. - Partner with Supply Ops to review the Production Plan weekly (PP, TCD, historical productive hours) and drive forecasting accuracy and adjustments. - Surface forecast risks and coordinate tactical interventions through the Ops Planning leads. Staffing & Resource Planning - Create/validate staffing requests and communicate staffing changes to Delivery teams, STOs and EMs. - Monitor account-level FTE budgets and employee spend; contribute to cost/benefit analysis of staffing vs. revenue. - Stress-test staffing scenarios to provide early warnings on headcount risks and to inform hiring / reallocation decisions. - Validate staffing tracking accuracy weekly; identify and correct discrepancies in project-level staffing data. Opportunity Forecast & Ingest - Ensure all revenue-generating opportunities are correctly logged in Opportunity Ingest (Linear) and that scoping is consistent. - Act as the owner for opportunities as they move through the ingest workflow — expedite translation of demand signals into the forecast (Airtable / PP / OF). - Run weekly checks to confirm Opportunity Forecast accuracy, and coordinate tactical scoping fixes when needed. Account Intelligence & Systems Enablement - Actively participate in core account meetings (ops, finance, GTM, delivery) as the Ops demand operational lead. - Design, build and maintain scalable planning tools and account-level reporting artifacts (ramp planning sheets, account dashboards, account playbooks). - Standardize and centralize information flow across customer ops pillars (demand forecasting, staffing, opportunity ingest). - Drive systems enablement, process documentation (SOPs), and handoffs that reduce manual work and increase reliability. Compensation packages at Scale for eligible roles include base salary, equity, and benefi

Scale AI is seeking a highly skilled and motivated Software Engineer, Frontier AI Infrastructure to join our dynamic Public Sector Engineering team. As a part of this team, you will own the model inference layer - enabling state of the art models, debugging the latest AI tools, managing networking, debugging latency, and tracking pricing/usage metrics for AI models. You will lead technical discussions on the frontlines with cloud vendors and customers to deliver on critical contracts and to debug platform issues. You will also work upstream with Product to understand features before they break, moving us from "infra-only debugging" to proactive integration testing. You will: - Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure. - Own services or systems and define their long-term health goals, while also improving the health of surrounding components - Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product. - You will work with Product to build integration tests that catch issues early, shifting the focus from "infra-only debugging" to preventing failures upstream. - Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. - Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings. Must have: - At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance Ideally you'd have: - Full Stack Development: Proficiency in both front-end and back-end development, including experience with modern web development frameworks, programming languages, and databases. Experience with developing & delivering software to air-gapped & isolated environments is a plus. - Cloud-Native Technologies: Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is desired. Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. - Security Focused: Experience with Federal Compliance frameworks, and requirements(e.g, Cloud SRG, FedRAMP, STIG Benchmarks, etc). Experience developing software & technical solutions that meet strict security & regulatory compliance requirements. - 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 evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends

ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. About the Team The Open Source team builds and maintains the core libraries that developers use to build AI agents with LangChain. The team works directly with the open-source community to improve the ecosystem, maintain core packages, and ensure developers can reliably build and deploy production AI systems using LangChain. About the Role We’re looking to add a core maintainer to the LangChain team. This person would be responsible for maintaining and improving the LangChain JavaScript package. In-person in SF preferred. What You'll Do - Improve the core abstractions and runtime of the LangChain and LangGraph JavaScript packages - Improve documentation across the LangChain ecosystem - Answer user questions and resolve issues from the developer community - Use LangChain to build example applications that demonstrate best practices for developers building AI systems What You'll Bring - 3+ years of software engineering or applied machine learning experience - A background in software engineering - Strong written and oral communication skills, with the ability to explain technical concepts clearly and concisely to both technical and non-technical stakeholders - The ability to thrive in a fast-moving environment and view unstructured environments as an opportunity to identify the most impactful work and help define the future success of the company - An ownership-minded approach to work, with the ability to manage your work efficiently and effectively without the need for close supervision Compensation - Annual salary range: $160,000- $240,000 Compensation Philosophy: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. We're looking for an experienced, driven Senior Strategic Account Executive to drive ElevenLabs' growth across Sweden's largest enterprises, with a specific focus on Financial Services. Our ideal candidate has a well-established network and a proven track record of selling to C-level and senior decision-makers at major Swedish financial institutions — and is passionate about the transformative possibilities of AI voice technology. In this role you'll act as a strategic partner and trusted advisor, enabling clients to leverage our industry-leading models and ElevenAgents — our end-to-end platform for building and deploying AI voice agents — to reimagine their customer experience, internal workflows, and monetization strategies. In this role you will: 1. Build and manage a growing portfolio of strategic accounts across Sweden, with a primary focus on Financial Services & Insurance, to help ElevenLabs meet its revenue goals. 2. Identify new business opportunities where ElevenLabs' conversational AI capabilities, including ElevenAgents, can drive customer engagement, contact center automation, and operational efficiency for financial services institutions. 3. Lead consultative, multi-stakeholder sales cycles, building compelling business cases that translate AI voice technology into measurable business value for senior executives and economic buyers. 4. Develop and

This posting is for candidates (interested in research intern roles) who attended ICML '26 and met with a member of our team. It was great meeting you at ICML 2026! Whether we chatted at our booth, during a poster session, or one of the workshops – we’re thrilled to connect with people who are passionate about pushing the boundaries of machine learning and AI. At Scale AI, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. We’re currently growing our team across multiple roles. If you're excited about the challenges we’re working on, drop your info here, and we’ll make sure someone from our team reaches out if there's a good fit with one of our open roles. Even if the timing isn’t quite right, we’d love to stay in touch. We look forward to continuing the conversation! In the meantime, you can read more about our research at https://labs.scale.com/ . https://scale.com/careers/university 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 application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. We comply with the United States Department of Labor's Pay Transparency provision . PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ ne

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. We're looking for an experienced, driven Senior Enterprise Account Executive to drive ElevenLabs' growth across Spain's largest enterprises. Our ideal candidate has a well-established network and a proven track record of selling to C-level and senior decision-makers at major Spanish organizations - and is passionate about the transformative possibilities of AI voice technology. In this role you'll act as a strategic partner and trusted advisor, enabling clients to leverage our industry-leading models and ElevenAgents - our end-to-end platform for building and deploying AI voice agents - to reimagine their customer experience, internal workflows, and monetization strategies. In this role you will: 1. Build and manage a growing portfolio of enterprise accounts across Spain - with a focus on Financial Services & Insurance, Telecommunications, Healthcare, and Utilities - to help ElevenLabs meet its revenue goals. 2. Identify new business opportunities where ElevenLabs' conversational AI capabilities - including ElevenAgents - can drive customer engagement, contact center automation, and operational efficiency. 3. Lead consultative, multi-stakeholder sales cycles, building compelling business cases that translate AI voice technology into measurable business value for senior executives and economic buyers. 4. Develop and maintain a deep understanding of the conversat

This posting is for candidates (interested in research intern roles) who attended ICML '26 and met with a member of our team. It was great meeting you at ICML 2026! Whether we chatted at our booth, during a poster session, or one of the workshops – we’re thrilled to connect with people who are passionate about pushing the boundaries of machine learning and AI. At Scale AI, our mission is to develop reliable AI systems for the world's most important decisions. For 10 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including: generative AI, defense applications, and autonomous vehicles. We’re currently growing our team across multiple roles. If you're excited about the challenges we’re working on, drop your info here, and we’ll make sure someone from our team reaches out if there's a good fit with one of our open roles. Even if the timing isn’t quite right, we’d love to stay in touch. We look forward to continuing the conversation! In the meantime, you can read more about our research at https://labs.scale.com/ . https://scale.com/careers/university 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 application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. We comply with the United States Department of Labor's Pay Transparency provision . PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ ne

ABOUT ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. We're looking for an experienced, driven Senior Strategic Account Executive to drive ElevenLabs' growth across Sweden's largest enterprises, with a specific focus on Retail. Our ideal candidate has a well-established network and a proven track record of selling to C-level and senior decision-makers at major Swedish retail and consumer brands — and is passionate about the transformative possibilities of AI voice technology. In this role you'll act as a strategic partner and trusted advisor, enabling clients to leverage our industry-leading models and ElevenAgents — our end-to-end platform for building and deploying AI voice agents — to reimagine their customer experience, internal workflows, and monetization strategies. In this role you will: 1. Build and manage a growing portfolio of strategic accounts across Sweden, with a primary focus on Retail and consumer brands, to help ElevenLabs meet its revenue goals. 2. Identify new business opportunities where ElevenLabs' conversational AI capabilities, including ElevenAgents, can drive customer engagement, contact center automation, and operational efficiency for retail organizations. 3. Lead consultative, multi-stakeholder sales cycles, building compelling business cases that translate AI voice technology into measurable business value for senior executives and economic buyers. 4. Develop and maintain a deep unders

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 Security organization protects the models, the infrastructure, and the people behind them, spanning platform and application security, detection and response, identity, compliance, corporate engineering and IT, insider risk, and physical security. The adversaries are well resourced, the assets are novel, and the consequences of getting it wrong extend beyond Anthropic. As Technical Recruiter, Security, you'll join the small team of recruiters who hire for that organization, owning full lifecycle recruiting for your searches and partnering with security leaders to turn ambiguous needs into clear search strategies. Security professionals are among the most heavily recruited people in technology, and earning their attention takes real domain fluency rather than a template. Key responsibilities - Own full lifecycle recruiting for a portfolio of roles across the Security organization, from intake through offer and close - Run structured intakes with security hiring managers, translating ambiguous needs into scoped requirements, calibrated bars, and search strategies - Build and maintain pipelines of specialized security talent, with an emphasis on passive candidates - Refine security interview loops, take-home assignments, and scorecards alongside hiring managers, your recruiting counterparts, and Recruiting Operations - Develop genuine domain fluency so you can hold a credible conversation with a detection engineer, a cryptographer, and a compliance lead in the same week - Advise hiring managers with market data and candid calibration feedback, and influence decisions through credibility rather than volume - Partner with Compensation, People Partners, and Mobility to structure equitable offers and guide candidates to close - Handle sensitive role and candidate information with discretion, including for searches whose scope is confidential Minimum qualifications - Deep full lifecycle recruiting experience, with substantial time supporting security, infrastructure, or comparably technical engineering organizations - Ability to hold a substantive technical conversation about security domains such as application security, cloud and infrastructure security, detection and response, identity, or compliance, and to evaluate technical qualifications rather than match keywords - Proficiency with an applicant tracking system like Greenhouse and other modern sourcing tools - Experience partnering directly with hiring managers on intake, bar calibration, and interview loop design - Sound independent judgment on candidate quality, and the ability to redirect a hiring manager away from pedigree and credential proxies toward the underlying competencies - Genuine interest in Anthropic's mission and in the role a strong security function plays in achieving it Preferred qualifi

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 Safeguards Analyst focusing on Integrity & Authenticity, you will be responsible for building and executing enforcement workflows for our products and services, with a focus on detecting and mitigating attempts to misuse Anthropic's AI systems for coordinated inauthentic behavior, election manipulation, and targeting, tracking, and surveillance of individuals. Your work will span a broad and interconnected set of harm areas: AI-enabled influence operations and disinformation campaigns, the abuse of AI to interfere with electoral processes, and the use of AI systems to facilitate stalking, surveillance, profiling, and the targeting of individuals or groups. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way. Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a political, violent, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays, particularly around major electoral events. Key responsibilities - Design and architect automated enforcement systems and review workflows that scale effectively while maintaining high accuracy - Partner with Engineering and Data Science teams to optimize detection models for policy violations and automated enforcement systems - Review flagged content to drive enforcement and policy improvements - Enforce usage policies with a focus on detecting and mitigating AI-enabled influence operations, coordinated inauthentic behavior, election interference, and targeting, tracking, or surveillance of individuals and groups - Support the Safeguards policy design team by providing detailed feedback on policy gaps based on real enforcement scenarios - Keep up to date with emerging AI policy enforcement best practices, evolving threat actor tactics, and the regulatory landscape around elections, privacy, and surveillance, using these to inform our decision-making and workflows Minimum qualifications - Experience in trust & safety, policy enforcement, threat intelligence, or a closely related field with a focus on one or more of: influence operations, disinformation, coordinated inauthentic behavior, election integrity, or privacy and surveillance harms - Experience standing up and scaling policy enforcement or content review workflows - Proficiency in SQL and/or other data analysis tools to draw insights from large datasets - Experience identifying emerging risks and threat actors, and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams - Experience working with generative AI products, including writing effective prompts for content review and enforcement - Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space Preferred qualifications - Experience conducting cross-platform i

About the Team OpenAI’s AI Deployment Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. About the Role As an Enterprise AI Deployment Engineer, you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role is based in our SF or NYC office. 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: - Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria. - Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes. - Work hands-on in code to build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators. - Make sound technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance. - Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers toward resolution. - Help customers progress from promising prototypes to reliable production systems, sustained adoption, and scaled impact. - Partner closely with customer engineering teams and OpenAI Product, Research, Engineering, Security, and go-to-market teams, translating deployment experience into high-signal product feedback. - Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments. You’ll thrive in this role if you: - Have a demonstrated track record of designing, building, and delivering AI or machine-learning systems in enterprise environments, including taking systems from prototype to production. Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, customer engineering, solutions architecture, or technical consulting. - Can point to substantial personal contributions in code, architecture, evaluation, debugging, or production engineering—not only program or stakeholder management. - Are highly proficient in Python and comfortable working across an AI application stack; experience with JavaScript, TypeScript, or another relevant language is valuable. - Understand how to evaluate AI systems systematically using representative data, graders, production signals, and human judgment. - Have navigated enterprise production requirements such as integrations, reliability, observability, security, privacy, data governan

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 Technical Program Manager for Inference, you'll be the critical bridge between our inference systems and the broader organization. You'll drive strategic initiatives across inference runtime and accelerator performance—coordinating model launches, managing cross-platform dependencies, and ensuring reliability across multiple hardware targets. This role is essential for keeping our most contended infrastructure teams shipping effectively while Research, Product, and Safety all depend on their output. Responsibilities: - Systems Integration & Coordination : Lead cross-functional initiatives for new infrastructure integration, establishing clear ownership, timelines, and communication channels between teams. Drive end-to-end planning for major infrastructure transitions including platform modernization and new tech adoption. - Performance & Efficiency: Partner with engineering teams to identify optimization opportunities, track performance metrics, and prioritize work that unlocks capacity gains. Coordinate across runtime and accelerator layers to ensure efficiency wins ship without compromising reliability. - Launch Coordination: Drive end-to-end readiness for model and feature launches across multiple hardware platforms. Establish processes for cross-platform validation, manage launch timelines, and ensure smooth handoffs between runtime, accelerator, and downstream teams. - Strategic Planning: Own and prioritize the inference deployment roadmap, working closely with engineering leadership to prioritize initiatives and manage dependencies. Provide visibility into upcoming changes and their organizational impact. - Stakeholder Communication: Build strong relationships across research, engineering, and product teams to understand requirements and constraints. Translate technical complexities into clear updates for leadership and ensure alignment on priorities and timelines. - Process Improvement: Identify inefficiencies in current workflows and drive systematic improvements. Establish metrics and dashboards to track infrastructure health, capacity utilization, and deployment success rates. You may be a good fit if you: - Have several years of experience in technical program management, with proven success delivering complex infrastructure programs, preferably in ML/AI systems or large-scale distributed systems - Have deep technical understanding of inference systems, compilers, or hardware accelerators to engage substantively with engineers and identify technical risks. - Excel at creating structure and processes in ambiguous environments, bringing clarity to complex cross-team initiatives - Have strong stakeholder management skills and can build trust with both technical and non-technical partners - Are comfortable navigating competing priorities and using data to drive technical decisions - Have experience with infrastructure s

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 responsible for enforcing our policies, protecting users, and ensuring our platform is not misused. As a Safeguards Enforcement Analyst focused on Safety Evaluations, you'll play a central role in ensuring our models meet safety and policy standards before and after launch. You'll run and monitor evaluations, drive mitigations when issues surface, coordinate the creation of new evals, and help build the processes and documentation that allow the team to scale this work over time. This role requires someone who is detail-oriented, comfortable navigating ambiguity, and capable of coordinating across teams to break new ground and drive work to completion. This work is deeply cross-functional — you'll partner closely with policy experts, Safeguards engineering teams, and many other stakeholders throughout the organization to ensure our evaluations are comprehensive and current, and that findings translate into meaningful improvements to model behavior. Responsibilities - Support model launch readiness by running evaluations, monitoring and interpreting results, and surfacing regressions or unexpected behavior changes to relevant stakeholders - Partner closely with policy and domain experts throughout the evaluation lifecycle — from identifying risks and scoping the right evaluation approach, to coordinating creation of new evals and ensuring existing ones remain current with evolving policies, threat vectors, and model capabilities - Work with cross-functional stakeholders to help manage evaluation outcomes, including interpreting results and driving mitigations where needed - Think strategically about eval quality to build processes and eval paradigms that keep evaluations unsaturated, high-signal, and insightful as models improve - Build out processes and frameworks for creating product-specific evaluations as Anthropic's product surface area expands - Help design and scope tooling improvements that accommodate evolving eval needs and expand self-serve eval creation and iteration for non-technical users - Write and maintain rigorous documentation for evaluation creation, execution, and interpretation as the team builds out eval tooling and processes You may be a good fit if you: - Have experience in trust and safety, content operations, policy enforcement, or a related operational role at a technology company - Thrive in ambiguous, fast-moving environments — you're energized rather than frustrated when the path forward isn't clearly defined and you need to figure it out as you go - Have experience building processes, workflows, or programs from scratch (zero-to-one work), not just maintaining existing ones - Have strong program management instincts, naturally creating structure around complex, multi-stakeholder efforts by tracking timelines, dependencies, and deliverables to keep work on track - Are eager to expand your technical toolkit, including adopting internal tools and AI-assisted workflows (e.g., Claude Code) to accelerate your work - Can manage multiple concurrent workstreams across different domain areas without losing track of details — strong prioritization and cont

Scale AI is seeking a highly skilled and motivated AI Data Engineer to join our dynamic Federal Engineering team. As a part of this team, you will play a critical role in supporting Scale’s government customers by scoping and developing onsite solutions. Our scalable, high-performance platform is the foundation for these customer solutions, and your expertise will be instrumental in designing and implementing systems that can handle interactions with existing customer systems to help our products integrate into existing customer workflows. You will: - Be open to >50% onsite work in a secured space. - Collaborate with cross-functional teams to define and execute the vision for data pipelines and agents. - Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back. - Deploy and maintain Scale software at customer sites. - Develop customer requested features and work closely with them to ensure that they win customer love. - Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions. - Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions. - This role will require at minimum an active Secret clearance and willingness to obtain a TS/SCI security clearance. Ideally you have: - 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 - Track record of success as a hybrid customer facing engineer, forward deployed software engineer, and ability to quickly adapt to different roles. - Prior experience developing with Python - 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 - Linux experience: Understanding of shell scripting, operating systems, etc - Networking experience: Understanding of networking technologies, configuration (ports, protocols, etc) is a plus. - 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 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 heal

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 Enforcement Analyst, you will be responsible for reviewing content and executing enforcement actions across our products and services, with a focus on detecting and mitigating attempts to misuse Anthropic's AI systems for malicious cyber operations. Your initial focus will center on reviewing flagged activity related to cyberattacks, malware development, and offensive exploitation; however, this position may later expand to include broader areas of enforcement. Safety is core to our mission, and you'll help uphold policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way. Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a violent, technical, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays. Key responsibilities - Review flagged content and accounts to make accurate, well-documented enforcement decisions in line with our usage policies - Detect and mitigate potential misuse of AI systems to facilitate cyberattacks, malware creation, exploitation tooling, and related harmful cyber operations - Triage and escalate novel, ambiguous, or high-severity cases to appropriate stakeholders - Provide detailed feedback to the Safeguards policy design team on policy gaps surfaced through real enforcement scenarios - Partner with Engineering and Data Science teams by surfacing detection model errors and quality signals from review to improve precision and recall - Maintain high accuracy and consistency standards across review queues - Keep up to date with emerging AI policy enforcement best practices, threat actor tactics, and the evolving cyber threat landscape, using these to inform enforcement decisions Minimum Qualifications - Experience in cybersecurity, including knowledge of offensive techniques, exploit development, malware analysis, or vulnerability research - Experience performing content review, abuse investigations, or policy enforcement at volume - Proficiency in SQL and/or Python for data analysis and threat detection - Experience identifying emerging risks and communicating findings to a diverse set of stakeholders, such as Product, Policy, Engineering, and Legal teams - Experience working with generative AI products, including writing effective prompts for content review and enforcement Preferred qualifications - Experience in trust & safety, abuse investigations, cybersecurity investigations, or threat intelligence in a technology or AI company - Experience with large language models and an understanding of how AI technology could be misused for cyber operations - Experience operating within abuse monitoring programs or enforcement review systems - Understanding of the challenges involved in implementing product policies at scale, including in the content moderation space - Experience working with government agencies,

The AI Development Strategist will report directly to vertical sales leadership and will play a critical role in generating and qualifying pipeline across Scale AI’s enterprise business. This is an education-focused, customer-facing role responsible for owning early-stage enterprise opportunities through Stage 2 qualification while helping prospective customers understand Scale AI’s capabilities, AI infrastructure offerings, and output methodology. This role is designed as a bridge into strategic enterprise AI sales and is ideal for experienced outbound or mid-market sales professionals looking to transition into enterprise AI sales. Unlike traditional SDR roles, this position is structured as a closing-capable role with meaningful ownership over customer engagement, qualification strategy, and pipeline generation. You will partner closely with Enterprise Account Executives and cross-functional teams to identify high-potential opportunities, educate customers on AI use cases, and ensure strong opportunity transition into active enterprise sales cycles. In this role, you will: - Own outbound prospecting and customer engagement through Stage 2 qualification - Drive customer outreach and education during the M1 phase of the sales process - Build trust with prospective customers and teach them about Scale AI’s capabilities, AI workflows, and output methodology - Lead discovery conversations to identify and qualify high-potential enterprise opportunities - Develop key narratives, content, and research through Stage 2 qualification - Lead cross-functional coordination, including with Solutions Engineering, Marketing, and GTM Strategy - Partner closely with Account Executives to transition qualified opportunities into active deal cycles - Develop a strong understanding of enterprise AI deployment patterns and customer use cases - Maintain strong CRM hygiene and pipeline visibility using Salesforce and related sales tools - Operate effectively within a verticalized sales organization across industries including Consumer, Financial and Professional Services, and Healthcare & Life Sciences (HCLS) - Thrive in a fast-moving environment while balancing customer education, outbound activity, and pipeline quality Ideally you will have: - 1-2 years of experience as an account executive OR 4+ years of experience in outbound sales, business development, SDR, BDR, or closing roles - Experience selling SaaS, infrastructure, data, or technical products preferred - Demonstrated ability to generate qualified pipeline and exceed activity or revenue targets - Strong consultative communication and discovery skills - Ability to educate customers on complex technical concepts and workflows - Strong intellectual curiosity and willingness to develop expertise in AI fundamentals and enterprise AI workflows - Experience working in fast-paced, high-growth environments - Excellent writing and verbal communication skills - Strong sales process and systems skills (Salesforce, Outreach, Slack, Clari preferred) - Demonstrated ability to collaborate effectively with cross-functional teams and sales leadership - Strong organizational skills, attention to detail, and ability to manage multiple opportunities simultaneously - Technical curiosity or familiarity with AI, machine learning, or data infrastructure highly valued <div class

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 ELEVENLABS ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $11B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. HOW WE WORK - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. WHAT WE OFFER - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development: ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel: We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working: If you’re not located near one of our main hubs, we offer a monthly co-working stipend. ABOUT THE ROLE We are looking for a Research Engineer to join the research team at ElevenLabs. You will thrive in the role if you enjoy doing the following: - Creating and upholding a reliable and expandable data management system specialized for text-to-speech projects. This includes establishing guidelines for versioning and ensuring data quality. - Establishing a streamlined process for autonomously training, assessing, and launching text-to-speech models. This encompasses implementing procedures for dynamic learning, as well as routines for fine-tuning and refreshing validation data. - Investigating cutting-edge approaches and strategies in machine learning, deep learning, and algorithms pertaining to text-to-speech technology. REQUIREMENTS We do not require any formal certifications, or degrees. Instead, we are seeking enthusiastic software engineers who can showcase solving impressively hard problems with artifacts such as past projects, designs, or GitHub contributions. We do require: - 3+ years industry experience as a Machine Learning Engineer, with a key emphasis on constructing data pipelines, as well as developing and implementing machine learning models. - Demonstrating the capacity to autonomously evaluate novel concepts or enhance current machine learning projects, with the potential outcome of contributing to published works. - Extensive backgro

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 Safeguards Enforcement Analyst on the user well-being team, you'll build and execute enforcement workflows that keep our products safe, with a focus on detecting and mitigating potential harm. Your initial focus will be on how Anthropic handles age. A core part of this work is making sure our consumer products reach the right audiences, including the detection signals, verification paths, and appeals workflows that keep underage users off surfaces not designed for them. Claude also reaches younger users through third-party developers building on our API, and you'll be the enforcement partner that sales and platform teams rely on when those customers need guidance on deploying age-appropriately. This position may expand into broader areas of user well-being enforcement over time. Safety is core to our mission, and you'll help shape policy enforcement so that our users can safely interact with and build on top of our products in a harmless, helpful, and honest way. Important context for this role: In this position you may be exposed to and engage with explicit content spanning a range of topics, including those of a sexual, violent, or psychologically disturbing nature. There is also an on-call responsibility across the Policy and Enforcement teams. Key responsibilities - Design and architect automated enforcement systems and review workflows that scale effectively while maintaining high accuracy - Partner with Engineering and Data Science teams to optimize detection models for policy violations and automated enforcement systems - Review flagged content to drive enforcement and policy improvements - Enforce usage policies with a focus on detecting and mitigating potential harmful use of AI systems - Work with Legal, Public Policy, and Privacy stakeholders to keep our age assurance approach proportionate, privacy-preserving, and responsive to an evolving regulatory landscape - Support the Safeguards policy design team by providing detailed feedback on policy gaps based on real enforcement scenarios - Keep up to date with emerging AI policy enforcement best practices, and use these to inform our decision-making and workflows - Responsible for Anthropic's layered age assurance approach - self-declaration, behavioral signals, verification, and ban appeals - to keep our first-party consumer products safe - Adjacent user well-being enforcement where age is a key factor in how policy is applied such as sexual content and illicit substances Minimum qualifications - Experience in trust and safety, online child safety, age assurance, privacy, product policy, or a related field - Subject matter expertise in one or more of: age assurance or age verification systems, age-appropriate design, child online safety, privacy-preserving verification methods, or content classification for young people - Experience driving cross-functional initiatives with Product, Engineering, Legal, and Policy partners — especially where safety, privacy, and usability tradeoffs need to be navigated together - Experience navigating evolving regulator

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 publishes risk reports: long-form technical documents laying out our assessment of the most serious potential risks from our models in domains like CBRN, cyber operations, and AI autonomy, along with the evaluation results behind that assessment, the safeguards we've applied, and our reasoning for why a given model is safe to deploy under our Responsible Scaling Policy. Some risk reports are standalone periodic assessments; others are more targeted, produced when we release a specific frontier model. These are some of the most consequential documents we produce, and one of the main ways we hold ourselves publicly accountable for the safety claims we make. We're hiring a Research Operations Specialist to own risk report operations. You'll be embedded with safety and research teams through each report cycle: coordinating contributions from dozens of researchers, holding the schedule and the open-threads list, and making sure the document ships on time as a single, internally consistent whole. You'll also do substantive editorial work, turning evaluation results, threat models, and researcher notes into clear prose and pushing back when a safety argument doesn't hold together. Risk reports sit within a wider family of external safety artifacts, including system cards and Responsible Scaling Policy updates. Part of this role is keeping those documents consistent with each other so that what we commit to in one place matches what we commit to and deliver on everywhere else. This role sits in Research Operations and works closely with our Frontier Red Team, Safeguards, Alignment, and capabilities researchers. The job is part project management, part translation: keeping a complex, many-author, hard-deadline document on track while making frontier risk assessment legible to researchers, policymakers, journalists, and the public without losing precision. Key responsibilities - Drive risk report production end to end: own the timeline, the contributor list, and the open-threads tracker - Coordinate core contributors across Frontier Red Team, Safeguards, Alignment, Interpretability, and capabilities research; chase drafts, resolve disagreements, find ground truth, and run the final polish pass - Edit (and sometimes write) content; work with researchers and red-teamers to turn evaluation results, threat models, and plots into clear, non-marketing prose, and keep Anthropic's voice consistent across sections drafted by many different people - Guard accuracy and consistency: catch terminology drift, risk claims that subtly contradict each other, and gaps between internal findings and what the draft says - Keep the risk report aligned with system cards, RSP disclosures, and other safety documentation, and flag conflicts early - Improve the process between reports; build templates, style guidance, and contributor checklists so each cycle starts from a stronger baseline - Pick up other research-adjacent operations and writing work related to our external artifacts and Anthropic's RSP Minimum qualifications - Demonstrated technical writing ability: can take dense, jargon-heavy source

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 a Technical CBRN-E Threat Investigator to join our Threat Intelligence team. In this role, you will be responsible for detecting, investigating, and disrupting the misuse of Anthropic's AI systems for Chemical, Biological, Radiological, Nuclear, and Explosives (CBRN-E) threats. We are particularly interested in candidates with deep expertise in either chemical defense or biodefense. You will work at the intersection of AI safety and CBRN security, conducting thorough investigations into potential misuse cases, developing novel detection techniques, and building robust defenses against threat actors who may attempt to leverage our AI technology for developing weapons, synthesizing dangerous compounds, or creating biological harm. Your specialized domain expertise will be critical to protecting against some of the most serious potential misuses of AI systems. Important context: In this position you may be exposed to explicit content spanning a range of topics, including those of a sexual, violent, or psychologically disturbing nature. This role may require responding to escalations during weekends and holidays. Responsibilities - Detect and investigate attempts to misuse Anthropic's AI systems for developing, enhancing, or disseminating CBRN-E weapons, pathogens, toxins, or other threats to harm people, critical infrastructure, or the environment - Conduct technical investigations using SQL, Python, and other tools to analyze large datasets, trace user behavior patterns, and uncover sophisticated CBRN-E threat actors - Develop CBRN-E-specific detection capabilities, including abuse signals, tracking strategies, and detection methodologies tailored to dual-use research concerns - Create actionable intelligence reports on CBRN-E attack vectors, vulnerabilities, and threat actor TTPs leveraging AI systems - Conduct cross-platform threat analysis grounded in real threat actor behavior, open-source research, and publicly reported programs - Collaborate with policy and enforcement teams to make informed decisions about user violations and ensure appropriate mitigation actions - Engage with external stakeholders including government agencies, regulatory bodies, scientific organizations, and biosecurity/chemical security research communities - Inform safety-by-design strategies by forecasting how threat actors may leverage advances in AI technology for CBRN-E purposes You may be a good fit if you - Have deep domain expertise in biosecurity, chemical defense, biological weapons non-proliferation, dual-use research of concern (DURC), synthetic biology, or related CBRN-E threat domains - Have demonstrated proficiency in SQL and Python for data analysis and threat detection - Have experience with threat actor profiling and utilizing threat intelligence frameworks - Have hands-on experience with large language models and understanding of how AI technology could be misused for CBRN-E threats - Have excellent stakeholder management skills and ability to work with diverse teams including researchers,

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 believe that learning is fundamental to human agency. Education Labs studies how people learn and build capability with AI, and we close the loop between what we discover and learning tools the world can access. As our first dedicated education researcher, you will strengthen how the team measures learning and AI fluency so our experiments produce trustworthy evidence. You will design the instruments, build the tools, run the studies, and translate findings into changes across our product experiments and action research in real learning settings. You operate at the frontier where the right measures often do not exist yet and have to be built. This is a hands-on research role embedded in a small team. You will publish and influence thinking across Anthropic, and you will also help decide what is working well enough to scale, what should be handed off to another team, and what should be spun down. We care about learning experiences that make people progressively more capable, curious, and empowered over time. Responsibilities - Design and run mixed-methods studies on how people develop real skill with AI, measuring success by capability growth rather than engagement. - Build and validate the instruments, measures, and evaluation methods the team relies on, so that findings hold up to scrutiny and can be trusted by research, product and policy partners. - Translate research insights into shipped product, curriculum, and model-level improvements through close collaboration with engineers, designers, and researchers. - Generate net-new insights about how AI is reshaping learning, and how communities and organizations can organize to learn alongside it. - Communicate your work through clear writing, prototypes, and presentations that shape thinking across the organization. - Create tools using code and software to collect validated metrics at scale. You may be a good fit if you have - A research background in learning sciences, education, cognitive science, HCI, educational psychology, or a closely related field, whether formal or self-directed. - Strong mixed-methods skills: experimental design, measurement and psychometrics, qualitative methods, and the judgment to choose the right approach for the question. - Hands-on technical skill in Python, data analysis, and working with LLMs, enough to run your own analyses and prototype new measures. - Comfort deriving insight from imperfect, dynamically changing data, and comfort making research decisions with incomplete information while holding a high bar. - Comfort with ambiguity and undefined problem spaces, plus a bias toward rapid, iterative inquiry and quick learning loops. - Clear communication and a track record of cross-functional collaboration with product, design, engineering, and research partners. <li

ABOUT THE TEAM The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem. We are dedicated to identifying emerging abuse trends, analyzing risks, and working with our internal and external partners to implement effective mitigation strategies to protect against misuse. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity. The Strategic Intelligence & Analysis (SIA) team provides safety intelligence for OpenAI’s products by monitoring, analyzing, and forecasting real-world abuse, geopolitical risks, and strategic threats. Our work informs safety mitigations, product decisions, and partnerships, ensuring OpenAI’s tools are deployed securely and responsibly across critical sectors. ABOUT THE ROLE As an Agentic Risk Analyst, you will shape OpenAI’s operating picture for current agentic risk across products and platforms. You will bring a strategic, system-level perspective to current risks, connecting individual incidents, technical findings, abuse patterns, and external developments to relevant workstreams, mitigations, owners, dependencies, and residual gaps. You will analyze how risks emerge through autonomy, multi-step task execution, tool use, memory, retrieval, connectors, computer-use capabilities, and multi-agent workflows, with a particular focus on both adversarial misuse and unintended system behavior. By synthesizing signals from investigations, evaluations, red teaming, security reviews, product launches, external research, and real-world incidents, you will maintain a current view of material risks and evolving threat patterns. Your work will help turn complex and often ambiguous signals into coordinated decisions and measurable follow-through across product, safety, security, policy, and governance teams. You will work closely with investigators, engineers, product, policy, safety, and security teams, and measurement and forecasting experts who lead longer-horizon risk discovery and scenario work to maintain a shared operating picture of current risks, mitigation priorities, owners, and dependencies. This is an opportunity to help shape how OpenAI coordinates decisions and follow-through across the evolving risk landscape of increasingly capable agentic systems, ensuring that safety decisions keep pace with rapidly advancing technology. IN THIS ROLE, YOU WILL: - Build and maintain a current, company-wide portfolio of material agentic risks across OpenAI’s products, platforms, and emerging capabilities, mapping each risk to relevant workstreams, owners, mitigations, dependencies, decisions, and residual gaps. - Run a cross-functional intake and review cadence for signals from across OpenAI and the broader ecosystem to identify emerging risks, evolving threat patterns, and important shifts in the agentic risk landscape, routing findings to the right owners and decision-makers - Connect individual incidents, technical findings, evaluations, and weak signals to broader system-level trends, producing clear assessments of impact, severity, evidence, uncertainty, priority, and recommended action. - Assess how emerging capabilities, product changes, ecosystem developments, and adversary adaptation may affect current risk priorities and launch readiness, surfacing risks that are unowned, stalled, or under-mitigated for decision and escalation, and tracking residual risk after launch. - In partnership with colleagues who lead horizon scanning, use relevant external developments across AI safety and security research, public incidents, adversarial activity, industry standards, emerging technologies, and competitor products, as inputs to current risk prioritization and mitigation decisions at OpenAI. - Apply and refine practical frameworks and taxonomies for current agentic failure modes, control gaps, abuse patterns, and potential downstream harms across products, deployment environments, and user workflows,

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 research organization works across the full model development lifecycle, from pre-training and post-training to alignment, interpretability, and safety, each operating at the frontier of AI development. As a Technical Program Manager for Research, you'll define and build the programs that research teams need most. You'll move across research areas like compute, evals, RL environments, and emerging research initiatives, going deep enough in each to understand how researchers work and what they need. You'll identify where the biggest opportunities for impact lie, find the highest-leverage gaps, and build the programs, processes, and tooling that allow researchers to focus on research. This is a 0-to-1 role: you'll explore new domains as priorities shift, determine what each one needs, and create lasting impact where none existed before. Note: This role may require responding to incidents on short-notice, including on weekends. Responsibilities - Embed deeply within a research domain to understand the technical landscape, build trust with researchers and technical leaders, and identify the highest-leverage problems to solve, knowing the surface area will shift over time as research priorities evolve - Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives, picking up new domains quickly and getting to depth fast - Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams, often without established playbooks or precedent - Establish processes and frameworks that bring structure to unstructured research environments without slowing researchers down - Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization across research and production workstreams - Drive eval readiness for model launches by standardizing results, shaping eval plans early, improving tooling, and ensuring honest, transparent reporting across research, product, and marketing - Own execution and operational health of RL environments across major training runs, coordinating cross-team trade-offs and feeding insights back into roadmap planning - Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations - Act as the connective tissue between research, engineering, and product teams to reduce chaos and accelerate execution You May Be a Good Fit If You - Have a background in ML research or engineering with several years of experience building technical programs from scratch, ideally with hands-on exposure to training, evaluation, or large-scale distributed systems - Are a fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers - Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments - Have a track record of operational ownership of c

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 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 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 researchers generate and depend on enormous amounts of data — training runs, evaluations, RL transcripts, annotations etc... The Research Data Platform team builds the systems that make that data easy to produce, find, query, and trust. We work in two modes: we build platform components that other systems plug into (for example, a metrics library that training frameworks integrate to record and retrieve run data), and we own core datasets end to end (for example, the data pipeline behind RL transcripts). As the team's tech lead, your job starts with our users. You'll work directly with researchers — and with the engineers who support them — to understand how they actually work, where managing data slows them down, and where a well-built platform component or a well-curated dataset would change what's possible. You'll turn what you learn into technical direction for the team, in partnership with the team's manager, who owns priorities and people. A central ambition you'll drive: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on, rather than every team managing its own copies. You'll spend your first few months close to the code and close to users: shipping improvements in our core systems, embedding with research teams, and building your own map of their workflows. As the team grows, this role has a natural path into formal people leadership for someone who wants it. Responsibilities - Work directly with researchers and the engineers supporting them to understand their workflows, identify the highest-leverage opportunities, and shape what the team builds next - Set the technical direction for the team across our platform and our datasets - Design and build platform components that other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks - Own core datasets end to end: the pipelines that produce them, the schemas that define them, and the documentation and guarantees that make researchers trust them - Drive convergence toward canonical datasets — including the core data model for RL transcripts — that research teams standardize on - Lead complex, multi-quarter projects that span several systems and teams, staying hands-on in the code - Raise the team's technical bar through design reviews, mentorship, and the quality of your own work You may be a good fit if you: - Have built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong instincts for data modeling and schema design that hold up as usage grows - Have set technical direction for a team, or owned the architecture of a data platform that other teams build on - Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping - Understand that researchers aren’t typical internal customers — the work is exploratory by nature, workflows differ from team to team, and requirements are discovered through experiments rather than specified up f

ABOUT US: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C https://modal.com/blog/modal-series-c at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. THE ROLE: Modal is the cloud platform built for AI. We're used by the world's leading AI labs, startups, and researchers to run compute-intensive workloads: training runs, inference, sandboxed code execution, and more. We need someone to own and grow our presence in academia and research. That means running our Modal for Academics program https://modal.com/academics: sponsoring courses and labs with compute credits, building relationships with professors and researchers at top institutions, and making Modal the default choice when someone needs GPUs for their next paper. The right person has lived in this world. You know how grant cycles work, how labs are structured, and how the conference publishing process actually runs. You've also shown you can operate beyond the lab, whether that's organizing events, building community, or working across institutional boundaries. In this role, you will: - Own and operate the Modal for Academics program end-to-end: handling inbound inquiries, overseeing grant decisions, onboarding, and follow-through. - Identify and pursue sponsorship opportunities with university courses, ML research labs, and academic conferences, with a bias toward work that's likely to be widely read and cited. - Build relationships with professors and researchers at top universities. - Partner with leading AI research labs on compute grants and collaborative programs. - Track outcomes: which grants produced papers, citations, talks, or downstream Modal adoption. - Represent Modal at academic conferences (NeurIPS, ICLR, ICML, and others) and research-adjacent events. - Develop outreach templates, program documentation, and supporting content to scale inbound interest. REQUIREMENTS: - A PhD or research Master's from a strong program in ML/CS or with a large computational component. You know how grants work, how labs are structured, and how the conference process actually runs. - Evidence that you've operated beyond pure research: organizing a workshop, running a student group, working with a grants office, industry internships, or similar. - Strong written and verbal communication, including on technical matters. You'll be representing Modal to professors and researchers at top universities. - Good judgment about what's worth pursuing. Not every course sponsorship or lab partnership is equal. - Organized and self-directed. This role spans dozens of active relationships at any given time. - Comfortable working in-person in a fast-paced startup environment. Nice to have: - Experience as a conference organizer, area chair, workshop chair, or reviewer at a major ML conference. - Existing relationships with pr

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, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences, focusing on raising the floor. About the role: We're looking for an Applied AI Engineer to join our Beneficial Deployments team. You’ll use your deep technical expertise to help partners accelerate their impact through advising on evals, hill-climbing on harnesses, prototyping new agents, etc. You will also work on building ecosystem-level tooling and infrastructure to scale impact beyond individual partnerships. Responsibilities: - Serve as a deep technical partner to mission-driven organizations through advising on evals, agent architectures, context engineering, cost optimization, and more - Provide hands-on support to partner engineering teams through pair programming, prototyping, and code contributions that accelerate their development - Develop public goods infrastructure that benefits entire ecosystems through benchmarks, MCP’s, and Agent Skills - Identify challenges unique to social impact partners, and contribute findings and improvements back to product, engineering, and research - Create technical presentations, demos, and scalable technical content (documentation, tutorials, sample code) to accelerate partner adoption and self-service - Help shape team processes and culture as we scale from 1 to N - Travel occasionally to customer sites for workshops, technical deep dives, and relationship building You might be a good fit if you have: - 4+ years as a Software Engineer, Forward Deployed Engineer, or technical founder - Production experience building LLM-powered applications, including prompting, context engineering, agent architectures, evaluation frameworks, and deployment at scale - Builder credibility that earns trust with technical founders and engineering teams—you've shipped products and can speak from experience - Experience working in ed-tech, healthcare, scientific research, nonprofit, or other mission-driven organizations, understanding their unique challenges and constraints - A love of teaching, mentoring, and helping others succeed - A scrappy mentality - comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission 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: £240,000 - £255,000 GBP <div class="content-conclusion"&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 We are seeking an experienced Finance Systems Integration Engineer to support our finance systems transformation at one of the fastest-growing AI companies. You'll design and build integrations connecting our ERP platform with critical financial applications and support our ERP implementation initiatives. As you master our integration landscape, you'll have opportunities to expand into Claude-powered AI automation and data pipeline development. You'll build the integration backbone for one of the fastest-growing AI companies, with a front-row seat to how Claude transforms financial operations. This is a foundational role where you'll shape our integration architecture from the ground up, then expand into cutting-edge AI automation as our needs evolve. You'll work alongside teams building frontier AI systems while directly applying that technology to solve real financial operations challenges. In this role you will: Core Focus: Integration Development & ERP Support - Design, build, and maintain integrations connecting ERP systems with downstream applications including ZipHQ, Brex, Navan, Clearwater, Payroll systems, Salesforce, and other critical financial platforms using Workato, MuleSoft, or similar iPaaS solutions - Support integration development and testing during the ERP implementation projects - Develop and maintain REST APIs, webhooks, and OAuth 2.0 authentication flows for secure system-to-system communication - Implement real-time and batch integration patterns supporting high-volume financial transactions - Establish monitoring, alerting, and error-handling frameworks to ensure integration reliability and data integrity - Document integration architectures, data flows, API specifications, and troubleshooting procedures - Collaborate with implementation consulting partners and vendors on technical integration requirements Additional Scope: AI Automation & Data Infrastructure As you master our integration landscape, you'll have opportunities to expand into: AI Agent Development - Build and deploy Claude-powered AI agents that automate financial operations including intelligent document processing, workflow automation, financial audit and reconciliations, and self-service reporting - Design agentic workflows that leverage Claude API capabilities integrated with ERP platform data and processes - Create automated validation and quality assurance processes for AI-generated outputs - Partner with Finance teams to identify automation opportunities and translate requirements into AI agent solutions Data Pipeline Support - Support data pipeline development using Airflow for workflow orchestration and dbt for data transformation - Build and maintain data flows from ERP and other financial systems into BigQuery for analytics and reporting - Implement data quality checks 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. 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

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 This is a senior, foundational role on a new team: you'll make architecture decisions the rest of the team builds on, and help shape what we build first. The work is hands-on and varied. Some weeks you'll be deep in pipeline or infrastructure engineering; others you'll be tuning prompts until the output is good, or sitting with a research team that depends on your systems and shipping the fixes they need. We're looking for experienced engineers who own outcomes end-to-end — down to reading transcripts, supporting users, and wrangling vendors. Anthropic's RL Data team builds the systems that produce high-quality reinforcement learning data for Claude: data collection pipelines, human feedback tooling, the execution environments RL tasks run in, and the quality assurance that keeps training data trustworthy at scale. Our goal is to make Claude great at real work — especially the work that matters most, like AI safety research and beneficial deployments of AI. (To be upfront: this is dual-use work — it advances general capabilities too.) Key responsibilities - Own significant parts of our stack end-to-end, from technical architecture through the unglamorous operational work that makes it succeed. - Build data collection pipelines, read the transcripts they produce, and iterate on prompts, evals, and graders until the output is good. - Develop and improve QA frameworks to catch reward hacking and ensure environment quality. - Build interfaces that make collecting human data fast and painless for the people providing it. - Harden execution environments — sandboxing, snapshotting, tool coverage — so tasks hold up at training scale. - Embed with the teams and domain experts who use our systems day-to-day, and work with operations, security, and compliance partners to roll our systems out to new users and vendors. Minimum qualifications - A track record of owning major projects end-to-end in fast-paced, ambiguous environments — for example as a founder or CTO, forward deployed engineer, tech lead, founding engineer at a startup, or creator of a substantial open-source project. - Trusted to run key projects: you lead and inspire others, plan workstreams effectively, collaborate with cross-functional stakeholders, and proactively eliminate or escalate blockers. - Strong software engineering skills in at least one modern programming language — we mostly use Python and TypeScript, but care more that you pick new tools up quickly than that you know our exact stack. Familiarity with Docker, Kubernetes, and common cloud infrastructure is a plus. - Effective use of AI tools in your own day-to-day work. - Care about the societal impacts of your work. Preferred qualifications - Experience with reinforcement learning on LLMs, particularly on the data side: creating evals, environments, rewards, graders, or training data. - Experience helping organizations use AI more effectively, including integrating wit

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 are hiring Research Associates: early-career bench scientists who will join a small team to help establish our biology research program. This is an entry-level role, great for individuals who have done some research during an undergrad or masters program and may be considering pursuing a PhD in Life Sciences in the future. You'll work directly alongside senior scientists, executing and troubleshooting experiments at the bench while learning advanced techniques in a fast-moving, highly collaborative environment. We value curiosity, careful hands, and the willingness to take ownership of a result. Our scientists are eager to teach; we're hiring for motivation and trajectory as much as for existing skill. This is a fully hands-on bench role — while familiarity with computational biology and bioinformatics is welcome, the role does not involve AI/ML model development. Key responsibilities - Execute molecular biology and biochemistry experiments at the bench under the direction of senior scientists - Maintain meticulous, reproducible records and contribute to shared protocols - Troubleshoot experiments, propose adjustments, and present results clearly in group meetings - Maintain mammalian and bacterial cell lines, materials stocks, and reagent inventories - Learn and adopt new techniques rapidly as projects evolve, with training provided Minimum qualifications - Have hands-on research experience in molecular biology, biochemistry, or a closely related field - Are proficient with foundational molecular biology techniques: PCR, gel electrophoresis, molecular cloning, and plasmid preparation &l

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 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. 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 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

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 US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes WHAT YOU’LL BRING - 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability,

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 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 the Team OpenAI’s Infrastructure organization builds and evaluates the systems that power advanced AI workloads. We work closely with hardware, modeling, and architecture teams to ensure that new platforms deliver real-world performance aligned with workload needs. Our team focuses on understanding workload behavior across evolving hardware platforms—bridging the gap between theoretical capability and observed system performance. About the Role We are seeking a Workload Porting & Performance Engineer to evaluate new hardware platforms by porting benchmarks and real-world workloads, analyzing performance, and identifying system bottlenecks. In this role, you will bring up workloads on new systems, characterize performance behavior, and adapt workloads to better utilize hardware capabilities. You will play a critical role in validating new platforms and ensuring that performance aligns with expectations across compute, memory, and networking subsystems. This role requires strong hands-on experience with performance analysis, workload optimization, and system-level debugging across hardware and software boundaries. 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. Key Responsibilities - Port and enable benchmarks and real-world workloads on new hardware platforms. - Evaluate system performance across compute, memory, storage, and networking subsystems. - Identify and analyze performance bottlenecks and inefficiencies. - Adapt and optimize workloads to better utilize hardware capabilities. - Develop and run performance experiments and profiling workflows. - Compare expected vs. observed performance and provide feedback to: - hardware architecture teams - performance modeling teams - system and software engineers. - Debug issues across the stack, including software, runtime, and hardware interactions. - Provide actionable insights to guide platform readiness and deployment decisions. Qualifications - Experience with performance analysis, benchmarking, or workload optimization. - Strong understanding of system architecture, including CPU/GPU, memory, and I/O subsystems. - Experience porting or adapting workloads across different hardware platforms. - Familiarity with profiling tools and performance debugging techniques. - Ability to identify root causes of performance issues across hardware/software boundaries. - Experience working in large-scale or distributed system environments. Preferred Skills - Experience with AI/ML workloads, including training or inference systems. - Familiarity with GPU or accelerator-based systems. - Experience working with low-level performance tools (profilers, tracing, microbenchmarks). - Background in systems software, compilers, or runtime optimization. - Experience collaborating with hardware and architecture teams on performance validation. 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 convict

ABOUT THE ROLE AND TEAM Joining us as a Research Engineer on the Post-Training team, you'll be diving into the exciting world of fine-tuning AI models, optimizing their performance, and ensuring they meet the highest standards of quality and efficiency. Your work will directly contribute to our groundbreaking advancements in AI, helping shape an era where technology is not just a tool, but a companion in our daily lives. At Character.AI http://Character.AI, your talent, creativity, and expertise will not just be valued—they will be the catalyst for change in an AI-driven future. The Post-Training team is responsible for developing our powerful pretrained language models into intelligent, engaging, and aligned products. As a Post-Training Researcher, you will work across teams and our technical stack to improve our model performance and training methods, including data, compute and algorithms. You will get to shape the conversational experience of millions of users per day. WHAT YOU'LL DO - Develop alignment algorithms and loss functions to improve data sample efficiency. - Write data pipelines to process diverse web data into a format models can ingest. - Identify quality signals to understand our model’s performance in the real world. - Design sampling algorithms to improve serving efficiency of large generative models. WHO YOU ARE - "All Industry Levels": have at least PhD (or equivalent) - 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 (reinforcement learning, transformers, etc) - Track-record of exceptional research or creative applied ML projects 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) - 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. 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 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 Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis. The Economic Research team is part of the Anthropic Institute , and studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data, including the Anthropic Economic Index, for the benefit of the public – helping policymakers, businesses, and workers understand and navigate the transition to powerful AI. The questions we work on include: how is AI changing jobs and economic activity, who is adopting it and why, and what determines whether a region or industry captures value from it. In this role, you will work closely with teams across Anthropic — including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy — to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting and implementing high-quality internal infrastructure, working in a fast-paced environment, and navigating ambiguity. Responsibilities : - Build and operate the data pipelines that turn raw usage data into clean, reusable, privacy-preserving datasets - Design new systems - including developing classifiers, training probes on model internals, and building the ML pipelines behind them — for understanding how Claude is used and the impact it's having on the economy - Build self-serve workflows to ingest and integrate external data sources so they're interoperable with internal datasets - Develop the APIs, libraries, and interfaces that serve data to researchers and the public - Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic's safety mission - Contribute to the team roadmap, documentation, and practices that enable self-serve data access while maintaining safety and governance standards - Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure You might be a good fit if you: - Have significant experience building data-intensive applications, pipelines, or internal tooling in production - Have experience with cloud infrastructure platforms such as AWS or GCP, and take pride in writing clean, well-documented code in Python that others can build upon - Have intuition for analytics workflows and empathy for how researchers and data scientists work - Are comfortable making technical decisions with incomplete information while keeping engineering standards high &l

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 As a member of the National Security Policy team at Anthropic, you will work directly with our most strategic national security customers and partners to drive transformational AI adoption. You will leverage your technical skills to architect innovative solutions that address our customers' business needs, meet their technical requirements, and provide a high degree of reliability and safety. In collaboration with the Sales, Product, Research, and Engineering teams, you’ll help national security partners develop strategies and implementation plans to integrate leading-edge AI systems into their mission. You will employ your excellent communication skills to explain and demonstrate complex solutions persuasively to technical and non-technical audiences alike. You also will play a critical role in identifying opportunities to innovate and differentiate our AI systems, while maintaining our best-in-class safety standards. We expect our team members to operate autonomously, thrive under ambiguity, and represent Anthropic at the highest level in customer environments. Core Responsibilities: - Act as a primary technical advisor for senior government leaders and prospective National Security customers evaluating Claude. Demonstrate how Claude can support U.S. and democratic allies’ national security operations and address customer use cases through proofs of concept. Provide technical guidance on integration, deployment, and adoption best practices. - Partner closely with the policy team and sales account executives to understand customer requirements. Develop customized pilots and prototypes, as well as evaluation suites to make the case for customer adoption. - Drive technical decision making by partnering on optimal setup, architecture, and integration of Claude into the customer's existing infrastructure. Demonstrate solutions to technical roadblocks. - Act as the voice of our customers and a key collaborator with our Product and Research teams to ensure we are delivering critical capabilities to the National Security community. - Travel to customer sites for senior leader meetings, AI implementation, technical enablement, and building relationships. - Establish a shared vision for creating solutions that enable beneficial and safe AI - Lead the vision, strategy, and execution of innovative solutions that leverage our latest models’ capabilities. You may be a good fit if you have: - Active TS/SCI security clearance (required) - 2+ years of experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Platform Engineer within the National Security space - Exceptional ability to build relationships with and communicate technical concepts to diverse stakeholders to include senior executives, engineering & IT teams, and more - Experience in the defense, technology, or cybersecurity industries - <p&g