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Head of Computer Vision and Machine Learning
About Dexterity: At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work. We’re starting with warehouse automation, where the need for smarter, more resilient supply chains impacts millions of lives and businesses around the world. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven, hardware-agnostic, and have already picked over 15 million goods in production. And did we mention we’re customer-obsessed? Every decision, large and small, is driven by one question – how can we empower our customers with robots to do more than they thought was possible? Dexterity is one of the fastest growing companies in robotics, backed by world-class investors such as Kleiner Perkins, Lightspeed Venture Partners, and Obvious Ventures. We’re a diverse and multidisciplinary team with a culture built on passion, trust, and dedication. Come join Dexterity and help make intelligent robots a reality! Required Skills: MS or PhD in Computer Science, or a related discipline, or equivalent experience. 2 or more years of experience leading a team of Computer Vision or Machine Learning engineers. At least 5 years of experience in building ML datasets, data pipelines and serving architecture. Creating a culture of developing high quality models, tests and regressions Strong knowledge of Modern C++ and Python. Experience building, maintaining and improving production code. Nice to haves: Experience with Docker and Kubernetes. Experience with cloud infrastructure such as AWS, GCP, Azure etc. and experience using this infrastructure to create training and serving pipelines. About the Role As the Head of CV and ML, you will be leading a team of highly qualified and talented Machine Learning and Computer Vision engineers working on Dexterity’s computer vision and data analysis initiatives. The role will involve understanding our current products and future requirements and bringing your substantial expertise to augment the products and help us to meet the future requirements. Previous experience leading CV and ML teams to create CV models, large training sets as well as robust regression pipelines is a prerequisite for this role. We are very interested in expertise around the subfields of semantic segmentation and instance segmentation and you will be involved in keeping up to date with the latest research papers in these areas and implementing them using platforms like PyTorch or TensorFlow. Robotics data also comes in a variety of different forms that are targets for models to help us predict and analyze behaviors emerging from our robotics systems. You will oversee the overall architecture and lead development of the perfect models, in addition to identifying additional opportunities throughout our system that can be solved with machine learning. In order to thrive in this role you will have significant experience with RGBD datasets and point clouds as they are important to the type of problems you will encounter in robotics. You will have solid experience in writing optimized code in c++ and python and mentoring other developers. You will have experience architecting models, training pipelines and serving infrastructure at scale. The end to end process of gathering and augmenting data, creating balanced training/validation/regression sets, studying the performance of different models and optimizing their quality, and finally deploying and troubleshooting the models as part of a complex robotics system will be under your supervision.
Research Engineer, Machine Learning Systems
COMPANY OVERVIEW Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. COMPANY OPERATING RHYTHM At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. THE OPPORTUNITY Voice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone. THE ROLE Deepgram is seeking a highly skilled and versatile Machine Learning Engineer to join our Research team. As a Member of the Research Staff, you will partner with research scientists to prototype and validate novel modeling ideas, then scale them through robust training systems for speech technologies, internal tooling, and innovative data strategies. You'll work at the intersection of machine learning, data infrastructure, and internal tooling to support our mission of building world-class speech recognition and synthesis systems. On the Research team, you will experiment with new technologies and techniques, while also working on product-focused deliverables, learning from colleagues with a wide range of expertise in AI and machine learning as you go. KEY RESPONSIBILITIES - Scalable Model Training: Architect and manage horizontally scalable systems that dramatically accelerate the end-to-end training lifecycle for Speech-to-Text (STT) and Text-to-Speech (TTS) models. This includes far more than automated training: the role focuses on making model development significantly faster and more efficient through optimized data preparation and management, high-throughput training pipelines, distributed infrastructure, and automated evaluation tooling. - Tooling & Accessibility: Design and implement internal UIs and tools that make ML systems and workflows accessible to non-technical stakeholders across the company. These UIs should be designed to provide transparency and flexibility to internally built tooling. - Infrastructure & Tools: Oversee and manage training tooling, job orchestration, experiment tracking, and data storage. THE CHALLENGE We are seeking Members of the Research Staff who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. IT'S IMPORTANT TO US THAT YOU HAVE - Strong experience with the machine learning research pipeline, particularly in STT or related speech domains. This includes experimenting with and evaluating new architectures and modeling approaches, and implementing large-scale training systems. - Proficiency with orchestration and infrastructure tools like Kubernetes, Docker, and Prefect. - Familiarity with ML lifecycle tools such as MLflow. - Experience building internal tools or dashboards for non-technical users. - Hands-on experience with data engineering practices for unstructured audio and text data. - Comfortable working in cross-functional teams that include researchers, engineers, and product stakeholders. WHY JOIN DEEPGRAM? At Deepgram, you’ll help shape the future of human–machine communication. Our research culture prioritizes ownership, experimentation, and real-world impact. As a Member of the Research Staff, you'll be empowered to build tools and systems that accelerate ML research and product deployment at scale. Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com http://deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
Sr. Specialist Solutions Architect
FEQ427R339 Location: Melbourne or Sydney As a Specialist Solutions Architect (SSA), you will be the trusted technical ML & AI expert to both Databricks customers and the Field Engineering organization. You will work with Solution Architects to guide customers in architecting production-grade ML & AI applications on Databricks, while aligning their technical roadmap with the continually evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying cutting-edge technologies in GenAI, MLOps, and ML more broadly, expanding your impact through mentorship, and establishing yourself as an AI thought leader. The impact you will have: - Architect production-level ML & AI workloads for customers using our unified platform, including agents, end-to-end ML pipelines, training/inference optimization, integration with cloud-native services, MLOps, etc. - Serve as a trusted practitioner for enterprise GenAI solutions, including RAG architectures, agentic systems (tool-calling agents, multi-agent orchestration, guardrails), natural language querying of structured data, AI evaluation and observability, and monitoring systems - Build, scale, and optimize customer AI workloads and apply best-in-class MLOps to productionize these workloads across a variety of domains - Provide advanced technical support to Solution Architects during the technical sale, ranging from feature engineering, training, tracking, serving, to model monitoring, all within a single platform, as well as participating in the larger ML SME community in Databricks - Collaborate cross-functionally with the product and engineering teams to represent the voice of the customer, define priorities, and influence the product roadmap, helping with the adoption of Databricks’ AI offerings What we look for: - 5+ years of hands-on industry ML experience in at least one of the following: - ML Engineer: Build and maintain production-grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring. - AI Engineer: Experience with the latest techniques in LLMs & agentic systems, including vector databases, fine-tuning LLMs, AI guardrail systems, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience - Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike - Passion for collaboration, life-long learning, and driving business value through ML & AI - [Preferred] 2+ years customer-facing experience in a pre-sales or post-sales role - Can meet expectations for technical training and role-specific outcomes within 3 months of hire - Can travel up to 30% when needed About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Senior Staff LLM Inference Engineer
At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI. D-Matrix Frontier Group sits at the leading edge of what’s possible with LLM inference on heterogeneous hardware. Our charter spans the full stack: from pathfinding emerging use cases and novel deployment patterns to deep optimization of inference kernels to building proof-of-concept systems that showcase D-Matrix’s unique computational fabric. We are an applied research and engineering team that moves fast, ships real systems, and works directly with product and hardware teams to shape the roadmap. We build the tools, runtimes, and frameworks that let frontier AI models run efficiently and cost-effectively across heterogeneous deployments combining D-Matrix silicon with CPUs, GPUs, and custom accelerators. Our work powers everything from benchmarking and evaluation pipelines to production-grade inference serving. This Role We are hiring end-to-end inference engineers who are comfortable going from a novel research idea to a deployed, optimized system. You will work at every layer of the inference stack from kernel-level optimization to distributed orchestration to high-level serving APIs. This role could be a great match for you if you: - Have deep intuition for modern generative AI architectures and how to squeeze performance out of them at inference time. - Are familiar with the internals of open-source inference frameworks (vLLM, SGLang, TensorRT-LLM, etc.) and can extend or replace them when needed. - Enjoy pathfinding new use cases — exploring heterogeneous deployment topologies and building early-stage POCs that prove out new ideas. - Are results-oriented with a strong bias toward action; you own problems end-to-end from prototype to optimization to handoff. - Are energized by working at the intersection of novel hardware and frontier models and want your work to directly influence how next-generation AI silicon is used. - Value clear communication and thrive in a small, high-ownership team environment. Responsibilities - Identify and prototype emerging LLM inference use cases suited to heterogeneous hardware deployments. - Build compelling proof-of-concept systems that demonstrate D-Matrix capabilities to customers, partners, and internal stakeholders. - Develop and tune custom kernels and operator-level optimizations to maximize throughput and minimize latency. - Drive quantization, sparsity, and batching strategies tailored to the D-Matrix computational model. - Build and maintain inference runtimes, serving frameworks, and evaluation tooling. - Contribute to distributed inference systems: tensor/pipeline parallelism, disaggregated prefill/decode, KV-cache management. - Work closely with hardware architects to provide firmware and compiler teams with actionable inference workload insights. - Partner with product and business development to translate POCs into customer-facing demonstrations. - Contribute to technical publications, whitepapers, and open-source projects that advance D-Matrix visibility. Required Qualifications - Bachelor’s degree in Computer Science, Electrical Engineering, or a related field, and 10+ years of relevant engineering experience or equivalent demonstrated experience. - Master’s or PhD in Computer Science, Electrical Engineering, or a related field preferred, with 6+ years of relevant industry experience. - Strong proficiency in Python and C/C++. - Hands-on experience optimizing LLM inference — attention kernels, KV cache, batching strategies, quantization (INT8/FP8/INT4). - Experience with at least one major inference framework (vLLM, SGLang, TensorRT-LLM, ONNX Runtime, or similar) at a contributor level. - Familiarity with GPU kernel programming (CUDA/Triton) and performance profiling tools. Preferred Qualifications - Experience with heterogeneous compute deployments — scheduling inference workloads across dissimilar hardware (accelerators, CPUs, GPUs). - Familiarity with custom silicon or ASIC-based inference (beyond GPU-only environments). - Experience with distributed inference: tensor parallelism, pipeline parallelism, disaggregated serving. - Contributions to open-source inference or ML systems projects. - Experience with production inference serving at scale (latency SLOs, continuous batching, multi-model serving). - Familiarity with speculative decoding, mixture-of-experts routing, or long-context-serving techniques. - Working familiarity with the material in the JAX Scaling Book or equivalent systems-level understanding of modern LLM training and inference. Why D-Matrix Frontier Group - Work on genuinely novel hardware: D-Matrix in-memory compute architecture opens up inference optimization problems that don’t exist anywhere else. - End-to-end ownership from idea to deployed system, with a short feedback loop between your work and real hardware. - Small, senior team with high autonomy and direct influence on product direction. - Competitive compensation, equity, and benefits in Santa Clara, CA. Equal Opportunity Employment Policy d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day. d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.
Applied AI/ML Engineer (Agents)
ABOUT CUSPAI CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering. We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock. We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters. We’re on the cusp of the on-demand materials era. Join us. THE ROLE Due to rapid growth in our core AI capabilities, we are seeking an experienced Applied AI/ML Engineer (Agents) to design and build the intelligent agents that power our autonomous materials discovery engine. YOUR IMPACT You will be instrumental in developing the "artificial brain" of our agentic materials discovery engine. This is responsible for orchestrating complex, closed-loop scientific workflows, autonomously making decisions, running simulations, and driving experimental campaigns to find breakthrough materials faster than ever before. Your work will directly accelerate CuspAI’s path to finding solutions for global sustainability challenges. WHAT YOU WILL DO Agentic systems - Design the agentic framework that powers our platform to discover new materials. This includes spanning dynamic, multi-stage simulation workflows from literature-grounded hypothesis generation through to computational and experimental validation - Build the integration that connects agents to ML models, simulation engines, databases, and heterogeneous compute backends - Design pipelines that let agents autonomously plan, schedule, execute, and interpret computational tasks at scale and over long periods of time - Use expert annotations from the Chemistry team to drive targeted improvements in agent planning, retrieval, and decision-making - Create evaluations to measure the effectiveness of agents Experimental design - Build agents that perform experimental design — applying Bayesian optimization, active learning, or related sequential decision-making methods to decide what to compute or measure next, and to balance exploration and exploitation across long-running discovery campaigns - Help close the loop between simulation and physical experiments so that outcomes become durable knowledge — feeding back into what agents know and how their models reason — compounding across campaigns. - Develop strategies for multi-fidelity and multi-objective decision-making, where agents must trade off cost, time, and uncertainty across simulations and physical experiments Interdisciplinary Collaboration - Work closely with Chemists, Materials Scientists, and the rest of the Agent team to co-develop our core orchestration intelligence - Work on customer projects and implement the direct needs required for these projects MUST HAVE SKILLS AND QUALIFICATIONS: - You are someone who gets excited about the opportunity to enable scientists to work on world changing challenges in this domain, with a personal interest in the potential applications of the technology that CuspAI is building. - Proficiency in the modern ML ecosystem such as PyTorch or JAX, with experience taking ML-driven systems from prototype to production - Strong software engineering skills (building systems at scale in a production environment): testing, modular design, CI/CD, and scalable ML operations in production environments - If you have a PhD or Masters degree and 4-5 yrs industry experience this would be ideal, but we will also consider you if you have a PhD and slightly less experience in industry - You have a proactive builder mentality with a bias toward shipping and iteration - Willingness to learn about materials science, and in particular experimental chemistry - to learn the vocabulary of the discipline to meaningfully interact with other stakeholders - Experienced in LLM-assisted programming, knowing its strengths and weaknesses thoroughly. BONUS POINTS (BUT NOT CRITICAL): - Experience applying ML models specifically for materials science, chemistry, or drug discovery applications - Experience with agentic frameworks and building LLM-powered applications - Experience with sequential decision-making methods — Bayesian optimization, active learning, bandits, or reinforcement learning — applied to real-world systems - Advanced agentic-reasoning techniques: planning models, self-improving systems, multi-tool agents, or RLHF/RLAIF workflows ADDITIONAL CONSIDERATIONS This role could be based in our Cambridge, London, Amsterdam or Berlin offices, with the expectation of being in the office three days per week. Additionally, there may be regular travel required to our other offices for collaboration and project oversight. Join us in shaping the future of materials with AI. Together, we can create groundbreaking solutions for a more sustainable world. WHAT WE OFFER - A competitive salary: We value and reward impact and growth - Equity in CuspAI: You have a stake in the success of the company - Time off to stay fresh: 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays - ‘Gold Standard’ parental leave: 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) at full pay - we look after you and your family while we work on the most important materials discovery problems together - Professional development budget: We invest in your career development so you can stay up to date with the latest industry knowledge or add to your skills to increase impact and growth - Solve meaningful problems: See how your work has a direct impact on advancing materials science and solving sustainability and climate-related problems through the creation and application of bleeding-edge SOTA technology and revolutionary techniques - True interdisciplinary teamwork: Be part of a deeply collaborative environment bridging AI research, computational chemistry, and experimental science - work with world-class researchers and engineers who enjoy sharing knowledge and supporting each other Join us in shaping the future of materials with AI. Together, we can create groundbreaking solutions for a more sustainable world. CuspAI is an equal opportunities employer committed to building a diverse and inclusive workplace. We do not discriminate on the basis of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding), veteran status, or any other basis protected by applicable law. We actively encourage applications from all backgrounds and value the unique perspectives and contributions that diversity brings to our team. Please let us know if you require any specific adjustments during or after the interview process. We will do everything we can within reason to accommodate.
Research Engineer, ML Infrastructure
WE ARE AN APPLIED AI LAB BUILDING END-TO-END SOFTWARE AGENTS. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-dense. Among our founding team, we have world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro. Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real-world tasks, apply to join us. ROLE MISSION Research moves at the speed of the infrastructure underneath it. Every training run, evaluation loop, and experimental iteration depends on systems that are fast, reliable, and built to scale. This role exists to make sure nothing in the stack becomes the bottleneck that slows down the frontier. You will own the core systems that researchers depend on daily: distributed training infrastructure, experiment orchestration, data pipelines, and the tooling that turns raw compute into usable research velocity. This is not a support role. You will work directly alongside researchers, understand the science deeply enough to anticipate what they need next, and build systems that hold up under the pressure of training jobs running across thousands of GPUs. We don't distinguish between research and engineering; the best infrastructure engineers here are also the ones who understand why the research works. WHAT YOU'LL ACCOMPLISH - Distributed Training Infrastructure: Build and own the systems that run large-scale training jobs reliably across GPU clusters. This includes job launchers, checkpointing and recovery, fault tolerance, and the monitoring that keeps researchers informed and unblocked. - Scaling Agent Rollouts: Own the infrastructure that runs hundreds of thousands of concurrent coding agent rollouts in VM sandboxes, from high-fidelity environment design to the distributed systems that hold up at our largest RL training scales. - Performance Optimization: Profile and improve training throughput end to end. Identify bottlenecks across data loading, communication overhead, memory utilization, and compute efficiency. Implement solutions that meaningfully improve step time and MFU at scale. - Experiment Orchestration and Tooling: Design and maintain the systems researchers use to launch, track, and analyze experiments. Reduce friction in the research loop so that more time is spent on ideas and less on waiting. - Data Pipeline Engineering: Build high-throughput, reliable data pipelines for training and evaluation. Ensure data quality, reproducibility, and efficiency at the scale our training runs demand. - Debugging and Reliability: Diagnose and resolve training failures across GPUs, networking, numerics, and data. Maintain detailed understanding of failure modes and build systems that fail gracefully and recover fast. - Parallelism and Systems Research: Implement and optimize parallelism strategies: data, tensor, pipeline, and sequence parallelism. Understand the tradeoffs deeply and apply them to get the most out of available hardware. - Scaling Infrastructure Ahead of Research: Anticipate what the research team will need next and build it before it becomes a constraint. The best infrastructure engineers here are proactive, not reactive. EXCEPTIONAL CANDIDATES HAVE DEMONSTRATED - Deep experience building and operating distributed training systems for large models; comfortable owning infrastructure end to end from the cluster level down to the training loop - Strong systems engineering fundamentals: distributed systems, networking, storage, and the ability to reason about performance across the full hardware-software stack - Proficiency in Python and C++; experience with PyTorch or equivalent deep learning frameworks at a systems level, not just API usage - Hands-on experience with GPU performance profiling, memory optimization, and compute efficiency; able to diagnose why a training run is underperforming and fix it - Experience implementing or optimizing parallelism strategies (data, tensor, pipeline, sequence) for large model training - Track record of building tooling and abstractions that meaningfully accelerate research workflows - Strong debugging instincts across complex, distributed systems where failures are non-deterministic and hard to reproduce - Enough ML knowledge to engage substantively with researchers: understand what they are training, why the architecture choices matter, and what the infrastructure needs to support - We care more about demonstrated capability than credentials. A PhD is one signal among many. RESOURCES & ENVIRONMENT - Small, highly selective team where research and product move together; prototypes reach real deployment quickly - You'll own and operate infrastructure running across thousands of GPUs; compute is not a constraint and neither is access to the systems you need to do the work well - The environment rewards speed, autonomy, and technical depth with minimal process overhead; this is one of the most competitive and fast-moving problems in AI EQUAL OPPORTUNITY Cognition is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process - please let us know if you need any.
Research Engineer, Mid-Training
WE ARE AN APPLIED AI LAB BUILDING END-TO-END SOFTWARE AGENTS. We're the makers of Devin, the first AI software engineer. Our team is extremely talent-dense. Among our founding team, we have world-class competitive programmers, former founders, and leaders from companies at the cutting edge of AI including Scale AI, Palantir, Cursor, Waymo, Tesla, Lunchclub, Modal, Google DeepMind, and Nuro. Building Devin is just the first step—our hardest challenges still lie ahead. If you’re excited to solve some of the world’s biggest problems and build AI that can reason on real-world tasks, apply to join us. ROLE MISSION Mid-training sits at the seam between pre-training and post-training and is one of the highest-leverage points in the entire model pipeline. This is where raw base model capability is sharpened into something that can reason deeply, generalize reliably, and serve as the foundation that post-training builds on. You will own the late-stage training decisions that determine what our models are fundamentally capable of: data mix and quality uplift, annealing schedules, context length extension, capability injection across coding, math, and reasoning, and the synthetic data strategies that make all of it scale. This role does cross-cutting work across what is classically considered both pre-training and post-training. We don't distinguish between research and engineering; we expect both. WHAT YOU'LL ACCOMPLISH - Data Mix and Quality Uplift: Design and iterate on high-quality data mixtures for late-stage and annealing training runs. Develop principled methods for sourcing, filtering, and weighting data to sharpen model capabilities without degrading general performance. - Capability Injection: Drive targeted improvements in coding, mathematics, and long-horizon reasoning through curated data strategies and training interventions. Translate research insights into measurable capability gains on our agents. - Synthetic Data Research: Develop and evaluate synthetic data pipelines that generate training signal at scale. Understand the limits and failure modes of synthetic approaches and build methods that hold up in production training runs. - Annealing and Schedule Design: Research and optimize multi-stage learning rate schedules, warmup strategies, and compute allocation across training phases. Understand how schedule choices interact with data distribution and model behavior. - Context Length Extension: Research and implement methods for extending effective context length without degrading short-context performance. This includes positional encoding strategies, data construction, and targeted evaluation. - Evaluation and Iteration: Build evals that distinguish real capability improvements from benchmark overfitting. Close the loop between training decisions and what actually matters for Devin and our other systems in deployment. - Scaling and Methodology: Measure how mid-training interventions scale with compute and data. Develop new approaches when existing methods hit ceilings; we expect both rigorous empiricism and original thinking. EXCEPTIONAL CANDIDATES HAVE DEMONSTRATED - Deep familiarity with the LLM training pipeline end to end: pre-training data, optimization, architecture, and how mid-training and post-training interact - Hands-on experience with continual pre-training, annealing, or late-stage data mixing for large models - Strong intuition for data quality: what makes a dataset useful for training, how to filter and curate at scale, and how data mix choices compound across evals - Experience developing or evaluating synthetic data pipelines for capability improvement - Proficiency in Python and deep learning frameworks (PyTorch); comfortable debugging distributed training at scale - Strong fundamentals in optimization, statistics, and ML theory; able to distinguish real effects from noise, instability, and overfitting - A track record of original contributions: publications, open-source impact, or internal results that moved a capability frontier - Comfort operating in ambiguous, fast-moving environments where the problem definition is as important as the solution - We care more about demonstrated capability than credentials. A PhD is one signal among many. RESOURCES & ENVIRONMENT - Small, highly selective team where research and product move together; prototypes reach real deployment quickly - Compute is not a constraint: large allocations with training jobs routinely running across thousands of GPUs from day one - The environment rewards speed, autonomy, and technical depth with minimal process overhead; this is one of the most competitive and fast-moving problems in AI EQUAL OPPORTUNITY Cognition is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic under applicable law. We are committed to providing reasonable accommodations for candidates with disabilities throughout the hiring process - please let us know if you need any.
Senior Software Engineer - Integrations - AI/ML
The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy: designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.
Staff Software Engineer, Inference API
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About the Role Cerebras is building a new generation of disaggregated AI inference systems that combine GPU-accelerated prefill with ultra-fast decode on the Cerebras Wafer-Scale Engine. We are hiring a Software Engineer to build and evolve the ML API layer that makes this heterogeneous serving system accessible, reliable, and easy to use. You will work across our inference APIs, model integration layer, request-routing services and Cerebras inference platform to deliver a consistent experience across models and accelerator backends. This role sits at the intersection of machine learning systems, API design, model serving, and distributed systems. You will enable new model architectures and inference capabilities, define stable user-facing behavior, and ensure that features such as streaming, sampling, tool use, structured outputs, multimodal inputs, and model configuration behave correctly and consistently in production. You will work closely with model enablement, compiler, runtime, cloud infrastructure, product, customer-facing teams, and customers directly. This is a hands-on software engineering role for someone who enjoys turning rapidly evolving ML capabilities into durable, production-quality APIs. Responsibilities - Build production ML inference APIs. Design, implement, and maintain APIs for chat completions, text generation, streaming, model configuration, tool calling, structured outputs, multimodal inputs, and other emerging inference capabilities. - Deliver a unified serving experience. Create consistent request and response semantics across GPU prefill, Cerebras decode, and other heterogeneous inference backends. - Enable new models and capabilities. Integrate emerging foundation models, tokenizers, prompt formats, sampling methods, attention variants, multimodal inputs, and model-specific features into the serving platform. - Own API compatibility and evolution. Maintain compatibility with widely adopted inference interfaces while designing Cerebras-specific extensions. Establish clear versioning, deprecation, validation, and backward compatibility practices. - Integrate with model-serving runtimes. Extend and integrate custom inference services with vLLM, PyTorch, Hugging Face libraries, the AMD ROCm stack, and Cerebras runtime components. - Support disaggregated inference. Build the control and data paths required to coordinate GPU prefill with Cerebras decode, including request routing, state transfer, error handling, retries, and lifecycle management. - Improve serving performance. Optimize streaming behavior, time to first token, request latency, throughput, batching, serialization, tokenization, scheduling, and communication between serving components. - Ensure functional and numerical correctness. Build validation systems for tokenization, sampling, logits, generated outputs, precision changes, model upgrades, determinism, and compatibility across serving backends. - Strengthen reliability and observability. Define end-to-end service indicators and build structured logging, tracing, metrics, dashboards, health checks, and diagnostic tooling for production inference traffic. - Develop testing and qualification infrastructure. Create conformance tests, workload-replay tools, model-validation suites, performance benchmarks, integration tests, and release gates. - Improve developer experience. Build intuitive configuration, SDKs, documentation, examples, debugging tools, and self-service workflows for internal developers, customers, and partners. - Collaborate across the stack. Partner with compiler, runtime, kernel, cloud, product, and solutions teams to translate model and customer requirements into scalable serving capabilities. Minimum Qualifications - 5+ years of software engineering experience, including substantial individual-contributor ownership of production software or distributed systems. - Strong programming ability in Python and Go plus experience developing performance-sensitive or highly concurrent services in C++, Rust, or a similar systems language. - Experience building stable APIs with clear validation, error handling, observability, compatibility, and versioning practices. - Experience integrating software across service, framework, runtime, and infrastructure boundaries. - Experience designing or maintaining OpenAI-compatible, gRPC, REST, or streaming inference APIs. - Experience with Linux, containers, Kubernetes or comparable orchestration systems, CI/CD, and operating latency-sensitive services in production. - Ability to diagnose correctness, reliability, and performance issues across multiple components of a distributed serving system. - Strong communication and cross-functional execution skills, with the ability to turn ambiguous model or product requirements into production-quality software. - Bachelor's degree in computer science, Computer Engineering, Electrical Engineering, or a related discipline, or equivalent practical experience. Preferred Qualifications - Experience modifying or contributing to vLLM, SGLang, PyTorch, Hugging Face Transformers, Triton, TensorRT-LLM, or another open-source ML systems project. - Experience creating API conformance, model-quality, numerical-comparison, determinism, or performance-regression test systems - Experience building SDKs, developer tools, model registries, configuration systems, or self-service ML platforms. - Experience with multi-model or multi-tenant inference platforms, including routing, admission control, fairness, quotas, rate limiting, and capacity-aware scheduling - Understanding of model-specific tokenization, chat templates, generation configuration, logits processing, stopping criteria, tool calling, structured generation, and constrained decoding. - Experience with disaggregated prefill/decode architectures, KV-cache transfer, prefix caching, chunked prefill, memory-aware admission control, or request scheduling. - Experience designing, building, or operating production APIs and services for machine learning, large language models, or other data-intensive applications. - Familiarity with reduced-precision inference and quantization formats such as BF16, FP8, FP4, INT8, or INT4. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.
ML Runtime and Kernel Engineer - Core ML
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About The Role The Core ML team develops novel machine learning algorithms that take advantage of the unique capabilities of the Cerebras Wafer-Scale Engine. Our work spans efficient LLM training and inference, parallel and diffusion-based generation, sparsity, scaling laws, and training dynamics. We are looking for an engineer to bridge the gap between promising research ideas and efficient execution on Cerebras systems. You will work across ML frameworks, compilers, runtimes, and low-level kernels to implement new algorithmic capabilities, diagnose performance bottlenecks, and turn research prototypes into robust, high-performance demonstrations. Depending on your background, your work may emphasize runtime capabilities such as token orchestration, scheduling, communication, and distributed execution; low-level kernel development for novel ML operations; or a combination of both. Responsibilities - Design and implement runtime components and high-performance kernels required by novel Core ML algorithms. - Translate research prototypes into efficient implementations for the Cerebras platform, including reference implementations and comparisons on GPUs where useful. - Profile and debug performance across the ML framework, compiler, runtime, communication, and kernel layers. - Optimize computation, memory movement, communication, and concurrency for large-scale training and low-latency inference. - Develop benchmarks, instrumentation, and automated tests that validate functionality, performance, and numerical correctness. - Collaborate closely with Core ML researchers and compiler, runtime, kernel, and inference engineers to evaluate design alternatives and deliver end-to-end capabilities. - Contribute to software architecture and roadmap decisions by identifying recurring limitations and high-leverage platform improvements. Skills & Qualifications - Bachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field. - Experience developing high-performance systems software, ML systems, runtimes, compilers, or computational kernels. - Strong programming skills in C++ and Python. - Solid understanding of parallel programming, memory management, concurrency, data structures, and performance optimization. - Proven ability to debug and profile complex software across multiple layers of a system. - Familiarity with modern machine learning architectures and frameworks such as PyTorch or JAX. - Ability to work effectively with researchers and translate evolving algorithmic requirements into reliable software. Preferred Skills & Qualifications - Experience with CUDA, Triton, low-level assembly, accelerator programming, or a C-like domain-specific language. - Experience with compiler internals, distributed runtimes, custom hardware interfaces, or HPC systems. - Understanding of machine learning fundamentals and ML systems, with the ability to reason about how algorithmic choices affect accuracy, systems implementation and performance. - Familiarity with LLM training or inference, including attention, KV-cache management, parallel generation, or distributed execution. - Experience developing software in an industrial or academic research environment where requirements evolve through experimentation. - Contributions to significant open-source systems, ML frameworks, compilers, or kernel libraries. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.
Full Stack LLM Engineer
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About the Role We are seeking a versatile and experienced engineer to join our Inference Core Model Bringup team. This team is responsible to rapidly bring up state-of-the-art open-source models (like LLaMA, Qwen, etc) or customer-provided proprietary models on our Cerebras CSX systems. Success in this role requires a system-minded generalist who thrives in fast-paced bringup environments and is comfortable working across the entire Cerebras software stack. Your work will play a critical role in achieving unprecedented levels of performance, efficiency, and scalability for AI applications. Responsibilities - Contribute to the end-to-end bring up of ML models on Cerebras CSX systems. - Work across the stack: model architecture translation, graph lowering, compiler optimizations, runtime integration, and performance tuning. - Debug performance and correctness issues spanning model code, compiler IRs, runtime behavior, and hardware utilization. - Propose and prototype improvements across tools, APIs, or automation flows to accelerate future bring ups. Skills & Qualifications - Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field. - Comfort navigating the full AI toolchain: Python modeling code, compiler IRs, performance profiling, etc. - Strong debugging skills across performance, numerical accuracy, and runtime integration. - Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and familiarity with model internals (e.g., attention, MoE, diffusion). - Proficiency in C/C++ programming and experience with low-level optimization. - Proven experience in compiler development, particularly with LLVM and/or MLIR. - Strong background in optimization techniques, particularly those involving NP-hard problems. What We Offer - Competitive salary and benefits package. - Opportunities for professional growth and career advancement. - A dynamic and innovative work environment. - The chance to work on cutting-edge technologies and make a significant impact on the future of AI. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.
Researcher, Post Training
ABOUT CARTESIA Our mission is to architect AI that learns from and interacts with the world like humans do. We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences. We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI. ABOUT THE ROLE The next leap in model intelligence won’t come from scale alone — it will come from better post-training and alignment. Cartesia’s Post-Training team is developing the methods and systems that make multimodal models truly adaptive, aligned, and grounded in human intent. As a Researcher on the Post-Training team, you’ll work at the intersection of machine learning research, alignment, and infrastructure, designing new techniques for preference optimization, model evaluation, and feedback-driven learning. You’ll explore how feedback signals can guide models to reason more effectively across modalities, and you’ll build the infrastructure to measure and improve these behaviors at scale. Your work will directly shape how Cartesia’s foundation models learn, improve, and ultimately connect with people. YOUR IMPACT - Own research initiatives to improve the alignment and capabilities of multimodal models - Develop new post-training methods and evaluation frameworks to measure model improvement - Partner closely with research, product, and platform teams to define best practices for creating specialized models - Implement, debug, and scale experimental systems to ensure reliability and reproducibility across training runs - Translate research findings into production-ready systems that enhance model reasoning, consistency, and human alignment WHAT YOU BRING - Deep knowledge of preference optimization and alignment methods, including RLHF and related approaches - Experience designing evaluations and metrics for generative or multimodal models - Strong engineering and debugging skills, with experience building or scaling complex ML systems - Ability to trace and diagnose complex behaviors in model performance across the training and evaluation pipeline NICE-TO-HAVES - Experience with multimodal model training (e.g., text, audio, or vision-language models) - Contributions to alignment research or open-source projects related to model evaluation or fine-tuning - Background in designing or implementing human-in-the-loop evaluation systems Note: Cartesia participates in E-Verify and will provide the federal government with Form I-9 information to confirm employment eligibility after hire. MORE DETAILS 🏢 In-office policy: We’re an in-person team based out of offices in 🇺🇸 San Francisco, 🇬🇧 London and 🇮🇳 Bangalore. We love being in the office, hanging out together, and learning from each other every day. 🌎 Visa sponsorship: We provide visa sponsorship support and assess each circumstance on a case-by-case basis. However, visa sponsorship is dependent on many factors, including the role you are applying for, and the location you are going to be based, and so we can't always guarantee success. Your Recruiter will work with you to understand your visa sponsorship needs from the first call. 🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way. 🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed. OUR BENEFITS (US EMPLOYEES ONLY) 💰 Compensation Competitive base salary alongside attractive equity package. 🩺 Health Insurance Fully covered medical insurance along with dental and vision for you and your family. 🧑🧑🧒🧒 Parental Leave 9 weeks paternity & 12 weeks maternity leave 🏦 401(k) 🚆 Commuter Allowance A monthly stipend to help you get to and from the office. 🏖️ Flexible PTO Take as much time as you need to recharge your batteries. 🍲 Meals & Snacks Lunch, dinner and plenty of snacks, provided daily. 🦖 Your own personal Yoshi OUR COMMITMENT TO EQUAL OPPORTUNITY Cartesia is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other legally protected status.
Researcher, Models (UK)
ABOUT CARTESIA Our mission is to architect AI that learns from and interacts with the world like humans do. We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences. We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI. ABOUT THE ROLE We’re opening our first ever office in Europe, and looking to hire incredible talent in London to advance our mission of building real-time multimodal intelligence. YOUR IMPACT - Conduct groundbreaking research in neural network architecture design to advance the state-of-the-art (SOTA) in alternative architectures (e.g., state space models, efficient Transformers, hybrid architectures). - Design novel architectures that improve model quality, inference efficiency, and adaptability across diverse deployment environments, from cloud to on-device. - Explore and develop capabilities such as statefulness, long-range memory, and innovative conditioning mechanisms for enhancing model expressiveness and generalization. - Investigate how architectural decisions impact model trade-offs, including scalability, robustness, latency, and energy efficiency. - Develop new frameworks and tools to evaluate architectural innovations, benchmarking performance across research and production settings. - Collaborate with cross-functional teams to translate architectural research into scalable and impactful systems for real-world applications. WHAT YOU BRING - Deep expertise in architecture design, with experience in researching or deploying advanced architectures (e.g., state space models, transformers, RNN variants, CNN variants). - Strong understanding of how architectures interact with system constraints, including deployment in cloud environments or on-device. - Proficiency in designing architectures that balance quality, efficiency, and adaptability across different use cases and modalities (e.g., vision, audio, text). - Familiarity with generative modeling paradigms like autoregressive and diffusion models, and designing capabilities such as statefulness and conditioning in deep learning models. - A proven research track record in top-tier ML/AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) or demonstrable contributions to state-of-the-art architectures. - Exceptional analytical and problem-solving skills, with a focus on experimentation and iterative refinement. - Strong programming skills in deep learning frameworks such as PyTorch or TensorFlow, and experience with profiling tools for understanding model performance. NICE-TO-HAVES - Prior research or publications in state space models, efficient Transformers or other alternative architectures. - Research or practical experience in designing architectures for multi-modal systems. - Early-stage startup experience or a track record of rapid innovation in R&D environments. MORE DETAILS 🏢 In-office policy: We’re an in-person team based out of offices in 🇺🇸 San Francisco, 🇬🇧 London and 🇮🇳 Bangalore. We love being in the office, hanging out together, and learning from each other every day. 🌎 Visa sponsorship: We provide visa sponsorship support and assess each circumstance on a case-by-case basis. However, visa sponsorship is dependent on many factors, including the role you are applying for, and the location you are going to be based, and so we can't always guarantee success. Your Recruiter will work with you to understand your visa sponsorship needs from the first call. 🚢 We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don’t sacrifice quality or design along the way. 🤝 We support each other. We have an open & inclusive culture that’s focused on giving everyone the resources they need to succeed. OUR BENEFITS (US EMPLOYEES ONLY) 💰 Compensation Competitive base salary alongside attractive equity package. 🩺 Health Insurance Fully covered medical insurance along with dental and vision for you and your family. 🧑🧑🧒🧒 Parental Leave 9 weeks paternity & 12 weeks maternity leave 🏦 401(k) 🚆 Commuter Allowance A monthly stipend to help you get to and from the office. 🏖️ Flexible PTO Take as much time as you need to recharge your batteries. 🍲 Meals & Snacks Lunch, dinner and plenty of snacks, provided daily. 🦖 Your own personal Yoshi OUR COMMITMENT TO EQUAL OPPORTUNITY Cartesia is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, or any other legally protected status.
Software Engineer - Training Product
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We’re looking for a customer-obsessed software engineer to come ship with us. You’ll own features like multi-node training and products like serverless reinforcement learning (RL) from conception to MVP (and from MVP to GA!). You’ll work through the stack, architecting solutions from API and UI down to our infrastructure layer. You’ll fine tune models yourself to develop an understanding of user workflows. You’ll work closely with research engineers leveraging state-of-the-art training techniques to build experiences that accelerate model development and solve for real pain points. If you’re excited to dive deep into the training, let’s talk! THE PRODUCT Take a look at what we’ve built so far: - Overview of the product so far https://www.baseten.co/blog/baseten-training-is-ga/#training-is-now-ga - Training docs overview https://docs.baseten.co/training/overview - Story of the Training product https://www.baseten.co/blog/a-q-a-from-inference-to-training-the-inside-story-of-baseten-s-newest-product/ - Research we've done https://www.baseten.co/resources/research/ EXAMPLE INITIATIVES - Checkpointing Pipeline: Our checkpointing pipeline starts with automated checkpointing, a feature that ensures that versions of models created during training are automatically backed up to the cloud. Users are able to then deploy checkpoints seamlessly into inference servers, providing point-and-click integrations into inference frameworks like vLLM and Baseten’s Inference Stack. This enables customers to quickly evaluate the performance of their checkpoints with real traffic. - Multinode training: Multinode training enables customers to easily run training jobs across multiple compute nodes, enabling users to train large models like GLM 4.7 and DeepSeek. We’ve built deeply at the Kubernetes layer to ensure that scheduling, startup, inter-node communication, and shutdown happen seamlessly under the hood and as the user expects. - Training DX: Customers come to train on Baseten because it helps them get to value fast. To do this, we ensure that the features we ship aren’t just fast, but are easy to iterate with. We enhanced Baseten’s metrics from pod-level GPU summaries to per-GPU and per-Node. We’ve built a CLI experience that caters to terminal users, and UI experiences that enable user to seamlessly manage their training jobs. RESPONSIBILITIES - Iterate like crazy - Design ergonomic APIs and abstractions to model complex resources and lifecycles - Work throughout the stack (API layer, backend and database implementation, infra layer; frontend is a plus) to implement features. - Fine-tune and deploy models to develop intuition around training workflows. - Partner closely with model developers and world-class research engineers to understand the requirements and pain points of post-training workflows. - Drive long-term improvements to improve reliability of systems and velocity of development - Fix bugs & resolve customer issues with urgency REQUIREMENTS - 5+ years experience building software applications - Deep knowledge of the web stack, databases, and distributed systems - Experience developing developer tooling or infrastructure products for external or internal users. - Good taste in product, particularly developer-oriented tools - Interest in ML/AI infrastructure and willingness to learn - Driven by high agency and ownership - Strong communication skills with the ability to bridge technical depth and business needs NICE TO HAVE - Experience launching features and products through different release cycles (MVP, Beta, GA, etc.) - Experience with model development methods and paradigms, like Supervised Fine-Tuning, Reinforcement Learning, Synthetic Data Generation, LoRA, Full Finetunes, etc. - Familiarity or experience with the open source training stack and frameworks (NCCL, PyTorch, Megatron, NemoRL, VeRL, Axolotl, HF Trainer) and distributed training techniques (FSDP, DeepSpeed). - Experience developing AI products, tooling, or agents - Frontend fluency BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Software Engineer - AI Developer Productivity
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Baseten's engineers want to work in an AI-first way. What's missing isn't enthusiasm — it's the platform underneath it. Today everyone assembles their own agent config, context files, and MCP servers, so the good patterns stay trapped in individual setups instead of becoming defaults everyone inherits. You'll build that platform: the agent configurations tuned to our monorepo, the context and tooling layer that makes agents competent in our codebase, the evals that tell us which approaches actually work, and the rollout mechanics that get a new engineer productive with agents in week one. You are not here to mandate how engineers use AI — you're here to make the good path the easy path. Success looks like teams adopting what you build because it beats what they'd cobble together themselves, not because a policy requires it. Platform engineer, not AI evangelist. Ship infrastructure, measure it, kill what doesn't work, let adoption be the referee. The playbook for AI-first SDLC doesn't exist at any company yet. You'll write ours. EXAMPLE INITIATIVES Agent substrate — Repo-level context infrastructure that makes agents competent in our codebase (CLAUDE.md/AGENTS.md http://CLAUDE.md/AGENTS.md conventions, architecture and domain context, and the tooling to keep it accurate as code moves). Internal MCP servers giving agents scoped access to CI, observability, incident tooling, deployment state, and docs. Shared skills, subagents, and hooks that encode Baseten workflows. Sandboxed environments where agents can build and test safely. The golden path — Project templates and onboarding that ship with AI tooling configured and working. Self-serve infrastructure so teams build their own agents without you as the bottleneck. Gateway, auth, cost controls, and audit logging for internal model access. The feedback loop — Eval harnesses that answer "is this config better than that one" against real Baseten tasks, not vibes. Instrumentation of AI tool usage and its downstream effects on cycle time, review latency, and change failure rate. Honest reporting, including on what you built that didn't pan out. Agents in the SDLC — Automation where agents earn their keep: PR review triage, test gap-filling, incident context assembly, migrations and refactors, codebase Q&A. Integrating agents into CI/CD with guardrails that make it trustworthy. RESPONSIBILITIES - Own the internal AI developer platform end to end — architecture, build, rollout, operation, measurement. - Evaluate and integrate third-party AI coding tools (Claude Code, Cursor, Codex, and whatever ships next quarter), and build the context layer that makes them work against our monorepo. - Build frameworks that let other engineers create their own agents without deep LLM expertise. - Establish the evaluation practice for AI-assisted development at Baseten, and use it to drive investment decisions. - Drive adoption through developer experience — good defaults, clear docs, low friction — not mandate. - Embed with teams to find where AI genuinely unblocks them, then generalize those wins into platform capabilities. - Own the safety layer: permissions, secrets handling, audit trails, cost management. REQUIREMENTS - Have 4+ years of relevant industry experience building and enabling AI native SDLC - Strong proficiency in Python and/or Go, building tools other engineers depend on daily. - Hands-on experience with LLMs and agent frameworks — tool calling, MCP, context management, orchestration, failure handling. You've shipped something agentic that real people used, not just prototyped. - Deep personal fluency with AI coding tools and well-formed opinions about where they break down. - Platform mindset: you build for adoption and self-service, treat internal engineers as customers, and would rather ship a good default than write a style guide. - Developer tooling, CI/CD, and Kubernetes/Docker fundamentals. - Comfort with ambiguity — this space invalidates its own best practices every few months. - Excellent written communication. Much of your leverage is docs, templates, and examples that scale beyond conversations you're in. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Staff Software Engineer, Deep Learning Acceleratio...
Who we are Aurora’s mission is to deliver the benefits of self-driving technology safely, quickly, and broadly. The Aurora Driver https://aurora.tech will create a new era in mobility and logistics, one that will bring a safer, more efficient, and more accessible future to everyone. At Aurora, you will tackle massively complex problems alongside other passionate, intelligent individuals, growing as an expert while expanding your knowledge. For the latest news from Aurora, visit aurora.tech http://aurora.tech or follow us on LinkedIn https://www.linkedin.com/company/auroradriver. Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. As a Staff Software Engineer focusing on Deep Learning Acceleration at Aurora, you will play a pivotal role in enhancing the performance of Deep Learning networks utilized in our Autonomous Vehicle (AV) systems. Your primary responsibility will be to conduct thorough performance analysis and optimization of these networks, ensuring they operate efficiently both onboard the vehicle and during training on large-scale data centers. This position requires a deep understanding of software architecture, system performance, and latency issues, as you will be tackling various challenges that arise in these areas. You will collaborate with a team of talented engineers and researchers to develop solutions that improve the overall efficiency and reliability of our self-driving technology. Your work will directly contribute to making transportation safer and more accessible. The role demands a strong analytical mindset, particularly in performance troubleshooting, where you will utilize techniques such as profiling and the roofline model to identify bottlenecks and optimize performance. In addition to your technical skills, you will need to be adaptable and quick to learn new technologies, as the field of deep learning and autonomous systems is rapidly evolving. Strong communication skills are essential, as you will be working in a fast-paced environment with large code bases and collaborating with cross-functional teams. In this role you will - Conduct performance analysis and optimization of Deep Learning networks running on the Autonomous Vehicle (AV). - Optimize software architecture, system performance, and latency for deep learning applications. - Work on deployment of deep learning models on the AV and training on large-scale data centers. - Troubleshoot performance issues using profiling and roofline model techniques. - Collaborate with cross-functional teams to enhance the efficiency of self-driving technology. Required Qualifications - Minimum 5+ years of professional experience in software engineering. - BS, MS, or PhD in Computer Science or a related field. - Strong programming skills in CUDA, C++ and Python - Extensive experience in high-performance computing and parallel programming, specializing in optimizing workloads to reduce GPU memory usage, minimize latency, and/or maximize throughput. - Proficiency in leveraging performance analysis tools such as NVIDIA Nsight Systems , Nsight Compute and applying techniques like roofline model for performance optimization. - Hands-on experience in optimizing DL/ML workloads at the framework level using at least one deep learning framework (e.g., PyTorch, TensorFlow), ensuring efficient and scalable model deployment. - Strong understanding of the fundamentals of computer vision and transformer-based deep learning architectures, with proficiency in foundational neural network building blocks. - Strong analytical skills for diagnosing and troubleshooting performance bottlenecks in complex systems. - Demonstrated ability to quickly learn and adapt to emerging technologies and tools in a fast-paced environment - Experience working on large code bases in a fast-growing environment. - Strong communication skills, enabling effective teamwork across multidisciplinary teams. - Comfortable working in Linux/Unix environments. Desirable Qualifications - Hands-on experience in motion planning or related fields such as robotics, autonomous systems, systems software, or computer vision. - Experience with TensorRT, OpenAI Triton, Mojo and other inference acceleration tools. For roles based in Mountain View, CA and San Francisco, CA: The salary range for this position is $189,000 - $274,000 per year. For roles based in Pittsburgh, PA: The salary range for this position is $171,000 - $247,000 per year. Aurora's pay ranges are determined by role, level, and location. Within the range, the successful candidate's starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. #LI-Mid-Senior Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks. We believe in-person work increases collaboration, empathy, and our ability to lead effectively. As a result, Aurorans are expected to work from the office at least 3 days per week, with some roles requiring more frequent in-office presence, up to 5 days per week, based on operational needs. Our Careers page https://aurora.tech/careers provides insight into what it is like to work at Aurora, and you can find all the latest updates in our Newsroom https://aurora.tech/newsroom. Our commitment to safety At the core of everything we do is our commitment to safety. Building best-in-class self-driving technology will take time, and we believe that each employee at Aurora has a role in contributing to safety, every step of the way. Aurora expects commitment to our safety policies from every employee, and seeks candidates who take an active responsibility, can contribute to building an atmosphere of trust, and invest in the organization’s long-term success by prioritizing working safely, no matter what. Our commitment to inclusion Aurora considers candidates without regard to their race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, pregnancy status, parent or caregiver status, ancestry, political affiliation, veteran and/or military status, physical or mental disability, or any other status protected by federal or state law. Aurora considers qualified applicants with criminal histories, consistent with applicable federal, state, and local law. We are also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, you may contact us at careersiteaccommodations@aurora.tech. For California applicants, information collected and processed as part of your application and any job applications you choose to submit is subject to Aurora’s California Employment Privacy Policy.
AI Sales Engineer, US
About Arize AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI & Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence. We’re a Series C company backed by top-tier investors, with over $135M in funding and a rapidly growing customer base of 150+ leading enterprises and Fortune 500 companies. Customers like Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works. The Team Our engineering team builds systems that interact with some of the most complex software ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. We are looking for an AI Sales Engineer (Solutions Architect) to join the good fight and get this product in the hands of every AI/ML organization. You'll work side-by-side with our Account Executives to articulate the Arize value and overall strategy to differentiate the platform throughout the sales process and drive POCs from requirements gathering to close. The activities performed by an AI Sales Engineer are diverse - one day you might be assisting with a high-level pitch of Arize to a CDO, and the next day you might be working hands-on to develop a small proof of concept. What You’ll Do You are the trusted advisor to the customer: - Partner with our account executives to articulate the overall Arize value proposition, vision, and strategy to customers - Work closely with some of the most sophisticated GenAI teams in the world. - You will act as a trusted advisor to our prospective customers, while also building relationships with technical and business stakeholders. - Lead product demonstrations of the Arize platform - Advise on GenAI and ML best practices - Lead discovery to understand prospect’s GenAI tech stack to collaborate with the Sales team to construct a compelling value proposition of the Arize Platform - Lead Proof of Concept (PoC) motions with prospective buyers - Handle technical objections and develop strategies across sales, engineering, and product to unblock them What We’re Looking For Note: Even if you do not check every single box, we still encourage you to apply! - 3+ years of experience in a customer-facing role such as sales engineer, solutions engineer, or forward deployed engineer - Strong communication skills: Ability to simplify complex, technical concepts - Proficiency in Python / Typescript - Knowledge of LLM / Agentic frameworks such as OpenAI Agents SDK, Google ADK, LangGraph, and DSPy - Understanding of GenAI concepts and application evaluation + development lifecycle - A quick and self learner - undaunted by technical complexity of production AI deployments and welcome the challenge to learn about them and develop your own POV. Bonus Points, But Not Required - Previous experience working across and aligning Sales, Product, and Engineering - Previous experience within a team that underwent a high growth stage - Previous engineering experience in: Data Engineering, MLOps, Kubernetes, GCP / AWS / Azure The estimated annual salary and variable compensation for this role is between $140,000 - $230,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support. While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces. More About Arize Arize’s mission is to make the world’s AI work—and work for people. Our founders came together through a shared frustration: while investments in AI are growing rapidly across every industry, organizations face a critical challenge—understanding whether AI is performing and how to improve it at scale. Learn more about what we're doing here: https://techcrunch.com/2025/02/20/arize-ai-hopes-it-has-first-mover-advantage-in-ai-observability/ https://arize.com/blog/arize-ai-raises-70m-series-c-to-build-the-gold-standard-for-ai-evaluation-observability/ Diversity & Inclusion @ Arize Our company's mission is to make AI work and make AI work for the people, we hope to make an impact in bias industry-wide and that's a big motivator for people who work here. We actively hope that individuals contribute to a good culture - Regularly have chats with industry experts, researchers, and ethicists across the ecosystem to advance the use of responsible AI - Culturally conscious events such as LGBTQ trivia during pride month - We have an active Lady Arizers subgroup
AI Sales Engineer, EMEA
About Arize AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimize their AI systems. That’s where we come in. Arize AI is the leading AI & Agent Engineering observability and evaluation platform , empowering AI engineers to ship high-performing, reliable agents and applications. From first prototype to production scale, Arize AX unifies build, test, and run in a single workspace—so teams can ship faster with confidence. We’re a Series C company backed by top-tier investors, with over $135M in funding and a rapidly growing customer base of 150+ leading enterprises and Fortune 500 companies. Customers like Booking.com , Uber, Siemens, and PepsiCo leverage Arize to deliver AI that works. The Team Our engineering team builds systems that interact with some of the most complex software ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. We are looking for an AI Sales Engineer (Solutions Architect) to join the good fight and get this product in the hands of every AI/ML organization. You'll work side-by-side with our Account Executives to articulate the Arize value and overall strategy to differentiate the platform throughout the sales process and drive POCs from requirements gathering to close. The activities performed by an AI Sales Engineer are diverse - one day you might be assisting with a high-level pitch of Arize to a CDO, and the next day you might be working hands-on to develop a small proof of concept. What You’ll Do You are the trusted advisor to the customer: - Partner with our account executives to articulate the overall Arize value proposition, vision, and strategy to customers - Work closely with some of the most sophisticated GenAI teams in the world. - You will act as a trusted advisor to our prospective customers, while also building relationships with technical and business stakeholders. - Lead product demonstrations of the Arize platform - Advise on GenAI and ML best practices - Lead discovery to understand prospect’s GenAI tech stack to collaborate with the Sales team to construct a compelling value proposition of the Arize Platform - Lead Proof of Concept (PoC) motions with prospective buyers - Handle technical objections and develop strategies across sales, engineering, and product to unblock them What We’re Looking For Note: Even if you do not check every single box, we still encourage you to apply! - 3+ years of experience in a customer-facing role such as sales engineer, solutions engineer, or forward deployed engineer - Strong communication skills: Ability to simplify complex, technical concepts - Proficiency in Python / Typescript - Knowledge of LLM / Agentic frameworks such as OpenAI Agents SDK, Google ADK, LangGraph, and DSPy - Understanding of GenAI concepts and application evaluation + development lifecycle - A quick and self learner - undaunted by technical complexity of production AI deployments and welcome the challenge to learn about them and develop your own POV. Bonus Points, But Not Required - Previous experience working across and aligning Sales, Product, and Engineering - Previous experience within a team that underwent a high growth stage - Previous engineering experience in: Data Engineering, MLOps, Kubernetes, GCP / AWS / Azure Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes unlimited paid time off, generous parental leave plan, and others for mental and wellness support. More About Arize Arize’s mission is to make the world’s AI work—and work for people. Our founders came together through a shared frustration: while investments in AI are growing rapidly across every industry, organizations face a critical challenge—understanding whether AI is performing and how to improve it at scale. Learn more about what we're doing here: https://techcrunch.com/2025/02/20/arize-ai-hopes-it-has-first-mover-advantage-in-ai-observability/ https://arize.com/blog/arize-ai-raises-70m-series-c-to-build-the-gold-standard-for-ai-evaluation-observability/ Diversity & Inclusion @ Arize Our company's mission is to make AI work and make AI work for the people, we hope to make an impact in bias industry-wide and that's a big motivator for people who work here. We actively hope that individuals contribute to a good culture - Regularly have chats with industry experts, researchers, and ethicists across the ecosystem to advance the use of responsible AI - Culturally conscious events such as LGBTQ trivia during pride month - We have an active Lady Arizers subgroup
Principal Robotics Machine Learning Engineer
Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better. JOB SUMMARY The Principal Manipulation Engineer is a core contributor to our robot’s ability to interact with the world with human-like world model context, dynamic perception, and interactive behavior. This role is responsible for the end-to-end lifecycle of learned manipulation, from data engine design through training to on-robot deployment and is expected to diagnose performance bottlenecks. This requires deep fluency in both classical computer vision and modern deep learning to unlock the full potential of state-of-the-art humanoid robot hardware. This role will bridge the gap between cutting-edge research and scalable, reliable production software. We move at startup pace; intense and focused, but sustainable. We operate with high ownership, fast feedback, and low bureaucracy. ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES - Implement and deploy state-of-the-art ML models/algorithms to achieve ambitious, world-class performance on open world object manipulation tasks with physical hardware. - Drive the data engine for manipulation learning: data collection strategy, curation, annotation, and synthetic/real data mix, in close partnership with data infrastructure teams. - Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning models on the robot. - Identify and prioritize performance bottlenecks, distinguishing between root causes traceable to classical vision (calibration, geometry, sensor fusion) vs those rooted in sub-optimal model/data, to prioritize fixes according to customer-expected levels of reliability. - Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders. SKILLS AND REQUIREMENTS Technical Skills (Must-Have) - Deployment Experience: 5+ years shipping machine learning models on robotic systems in production environments. - AI Model Development Proficiency: Expertise in models including CNNs, transformers - Software Engineering: Proficiency in Python, C++, and PyTorch, with experience building real-time robotic software stacks. - AI Data Pipeline Experience: Experience with data collection, annotation, and synthetic data generation for ML. - Computer Vision: Familiarity with 6D pose estimation, camera calibration/extrinsics, point cloud processing and visual-servoing. - Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic manipulators. Good to Have - ML Ops: Familiarity with distributed training and MLOps principles. - Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control. - Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation. - Logistics Automation Background: Experience using manipulators to automate dynamic real-world material handling tasks. EDUCATION and/or EXPERIENCE - BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field. - 7+ years of relevant experience (or 5+ years with a PhD) specifically focused on robotic manipulation or complex motion control. - A proven track record of taking complex algorithms from conception to successfully deploying them on physical hardware. PHYSICAL REQUIREMENTS - Prolonged periods of sitting at a desk and working on a computer - Must be able to lift 15 pounds at times - Vision to read printed materials and a computer screen - Hearing and speech to communicate *This is a direct hire. Please, no outside Agency solicitations. Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Senior Software Engineer - ML Infrastructure
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE We are looking for both infrastructure engineers with expertise in machine learning pipelines and ML engineers that want to work beyond modeling to join the Data & ML infra group. This role will work across the entire ML lifecycle (dataset generation, training frameworks, compute, evaluation, and deployment) and work directly with modeling teams. This team is a good fit if you are excited to work on broad, ambiguous problems and develop across the entire ML stack. At Applied Intuition, we encourage all engineers to take ownership over technical and product decisions, closely interact with external and internal users to collect feedback, and contribute to a thoughtful, dynamic team culture. AT APPLIED INTUITION, YOU WILL: - Design and implement distributed cloud GPU training approaches for deep learning model training and evaluation - Build end-to-end machine learning pipelines and integrate them into core product workflows - Encourage change, especially in support of ML engineering best practices, and maintain a high standard of excellence - Collaborate with engineers across the entire company to solve complex data problems at scale WE'RE LOOKING FOR SOMEONE WHO HAS: - A Bachelor's degree in Computer Science, Software Engineering, or equivalent - 3+ years of professional experience - Experience with building software components to address production, full-stack machine learning challenges. This is not purely a research problem - Opinions about building a company-wide platform for ML training, evaluation, and deployment - Knowledge of the open source landscape with judgment on when to choose open source versus build in-house - Excellent analytical and problem-solving skills NICE TO HAVE: - Experience with developing, running, and managing orchestration systems like Airflow and Flyte that non engineers can use to build data pipelines. - Experience with ML modeling frameworks (PyTorch, Tensorflow, etc.), and model serving platforms (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.) Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Please reference the job posting’s subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the location listed is: $215,000 - $285,000 USD annually. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Scientist - Reinforcement Learning, Robot...
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM We are looking for multiple passionate Research Scientists to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. AT APPLIED INTUITION, YOU WILL: - Conduct research on reinforcement learning (RL) related topics including large-scale closed-loop RL and VLA post-training with applications to robotics with emphasis on dexterous manipulation - Diving into fundamental and relevant topics on RL with broader applications - Work closely with other Research Scientists and interns on research publications for submission to top-tier conferences - Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and robotics products WE’RE LOOKING FOR SOMEONE WHO HAS: - Strong research record in the fields of RL and VLA post-training for robotics and autonomous systems, with publications in top-tier conferences or journals in the fields of computer vision, machine learning, and robotics - MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely-related fields - 1+ year of research experience in autonomy, self-driving, robotics, or related field - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training NICE TO HAVE: Hands-on experience in at least one of the following fields: - VLA post-training for autonomy or robotics - Large-scale closed-loop RL in robotic simulation - Large-scale RL training infrastructure Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Scientist - 3D Vision and Generation, Sel...
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM We are looking for multiple passionate Research Scientists to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. AT APPLIED INTUITION, YOU WILL: - Conduct research on 3D vision related topics including 3D/world-action foundation model, multi-modal pretraining, feed-forward Gaussian splatting, world foundation model with applications to autonomous driving, and fundamental topics on 3D vision and generation with broader applications - Work closely with other Research Scientists and interns on research publications for submission to top-tier conferences - Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and simulation products WE’RE LOOKING FOR SOMEONE WHO HAS: - Strong research record in the fields of 3D vision, reconstruction and generation for autonomous systems and robotics, with publications in top-tier conferences or journals in the fields of computer vision, machine learning, and robotics - MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely-related fields - 1+ year of research experience in autonomy, self-driving, or related field - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training NICE TO HAVE: Hands-on experience in at least one of the following fields: - 3D foundation model and pretraining - Multi-modal foundation model - Feed-forward Gaussian splatting and reconstruction - World foundation model and generation - 3D/multi-view end-to-end models for autonomous driving or robotics Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Engineer - Robot Learning
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM We are looking for multiple passionate Research Engineers to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. AT APPLIED INTUITION, YOU WILL: - Support the team robotic and data collection hardware set up, and design the mechatronic system for customized demands - Construct robotic simulation environments at scale and use it for RL training - Process human data for robotic use cases - Work closely with Research Scientists and interns on high-quality research publications to submit to top-tier conferences - Collaborate with our engineering teams on ADAS, data, and simulation to deploy end-to-end algorithms for mass production vehicles, and neural simulation/generation for tools supporting autonomy development WE’RE LOOKING FOR SOMEONE WHO HAS: - Hands-on experience in at least one of the following fields - World-action foundation model, video/Gaussian generation, diffusion policy, multi-modal robot learning - VLA post-training, reinforcement learning - Physics-aware reconstruction, deformable simulation - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong engineering and research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training - Masters or PhD in machine learning and computer vision with autonomy and robotics applications or closely related field - 1+ year of experience in autonomy and robotics applications or closely related field NICE TO HAVE: - Passion for building and shipping customer-focused software frameworks or tools Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Engineer - Reinforcement Learning, Self-D...
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM We are looking for multiple passionate Research Engineers to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. AT APPLIED INTUITION, YOU WILL: - Conduct research on reinforcement learning (RL) and its training infrastructure, including topics on large-scale self-play RL, VLA post-training, large-scale closed-loop RL based on neural simulation with applications to autonomous driving - Work closely with Research Scientists and interns on high-quality research publications to submit to top-tier conferences - Collaborate with our engineering teams on ADAS, data, and simulation to deploy end-to-end algorithms for mass production vehicles, and neural simulation/generation for tools supporting autonomy development WE’RE LOOKING FOR SOMEONE WHO HAS: - Hands-on experience in at least one of the following fields - Self-play RL and imitation learning, behavior learning - VLA post-training for autonomy or robotics - Large-scale closed-loop RL in driving simulation - Large-scale RL training infrastructure (Ray preferred) - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong engineering and research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training - Masters or PhD in machine learning and computer vision with autonomy and robotics applications or closely related field - 1+ year of experience in autonomy applications or closely related field NICE TO HAVE: - Passion for building and shipping customer-focused software frameworks or tools Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Engineer - New Grad (2027)
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions About the role and team We are looking for passionate early-career Research Engineers to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions, including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots, and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. This role is designed for recent PhD or MS graduates ready to translate strong academic research into shipped autonomy systems. AT APPLIED INTUITION, YOU WILL: - Collaborate with our ADAS stack engineering team to test and deploy end-to-end algorithms for mass production vehicles - Collaborate with our simulation team to deploy data-driven generation algorithms for highly efficient and automated tools - Conduct research on architecture design, robustness, safety, and language modality incorporation for our differentiable stack - Work closely with Research Scientists and interns on high-quality research publications to submit to top-tier conferences WE’RE LOOKING FOR SOMEONE WHO HAS: - Hands-on experience in at least one of the following fields: - World-action foundation model, video/Gaussian generation, diffusion policy, multi-modal robot learning - VLA post-training, reinforcement learning - Physics-aware reconstruction, deformable simulation - MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely related field, graduating by December 2026 or Summer 2027 - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong engineering and research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training - 1+ year of experience in any field, or equivalent combination of education and experience NICE TO HAVE: - Industry experience on relevant topics (self-driving application preferred) - Passion for building and shipping customer-focused software frameworks or tools Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Research Engineer - 3D Vision and Generation, Self...
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM We are looking for a passionate Research Engineer to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research http://appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. AT APPLIED INTUITION, YOU WILL: - Conduct research on 3D vision related topics including 3D foundation model, multi-modal pretraining, Gaussian splatting, world foundation model with applications to autonomous driving - Work closely with Research Scientists and interns on high-quality research publications to submit to top-tier conferences - Collaborate with our engineering teams on ADAS, data, and simulation to deploy end-to-end algorithms for mass production vehicles, and neural simulation/generation for tools supporting autonomy development WE’RE LOOKING FOR SOMEONE WHO HAS: - Hands-on experience in at least one of the following fields - 3D reconstruction with Gaussian splatting - 3D / multi-modal foundation model and its pretraining - World foundation model and video generation - 3D/multi-view end-to-end models for autonomous driving or robotics - Passion for next-generation, scalable autonomy and robotics for real-world systems - Strong engineering and research skills and the ability to work both independently and collaboratively on projects - Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training. - Masters or PhD in machine learning and computer vision with autonomy and robotics applications or closely related field - 1+ year of experience in autonomy and robotics applications or closely related field NICE TO HAVE: - Industry experience on relevant topics (self-driving application preferred) - Passion for building and shipping customer-focused software frameworks or tool Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Machine Learning Engineer, Customer Engineering
About Anyscale At Anyscale https://www.anyscale.com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=23&pp=iAQB, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=46&pp=iAQB, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date. About the role The Customer Engineer will play a crucial role in the customers’ post-sale journey - helping them to onboard, adopt and grow on Anyscale, troubleshooting and resolving open customer tickets and driving consumption. Anyscale is an ever evolving platform and hence will require close co-ordination with our engineering teams to debug complex issues. This is an exciting role for those who are technically curious and passionate about ML/AI, LLM, vLLM and the role of AI in next generation applications. It’s an opportunity to make a significant impact in a collaborative, fast-paced environment while building a new segment in this space. In this role, you’ll be able to - Resolve customer issues and help in their successful adoption of Anyscale platform - Be a technical advisor, and internal champion for our key customers - Own customer issues end-to-end, from troubleshooting, triaging, escalations and eventual resolution - Participate in our follow-the-sun customer support model to ensure continuity in resolving high priority tickets - Keep track of open customer bugs and feature requests to influence prioritization and provide timely customer updates upon resolution - Contribute towards improvement of internal tools and documentation of playbooks, guides and best practices etc. based on observed patterns - Habitually provide feedback and collaborate cross-functionally with product and engineering teams to address customer issues with a focus on improving the product experience - Build and maintain strong relationships with technical stakeholders within customer accounts Qualifications - 7+ years of experience in a Machine Learning role in a dynamic, fast-paced, startup-like environment - Strong organizational skills and ability to manage multiple customer needs simultaneously - Proficient at developing data pipelines for training, fine-tuning and inference/serving of LLMs - Experience running and optimizing infrastructure for distributed ML workloads on the major cloud platforms (AWS/EKS, GCP/GKE or Azure/AKS) - Excellent communication and interpersonal skills. - Strong sense of ownership, self-motivation and eagerness to acquire new skills and do new things - Willingness to up-level the knowledge and skills of your peers through mentorship, trainings and shadowing Bonus - Experience with Ray - Knowledge of MLOps platforms - Knowledge of container orchestration platforms (e.g., Kubernetes), infrastructure as code (e.g. Terraform), CI/CD tools (e.g. Github Actions) WHY ANYSCALE - We're on a mission to make scalable computing effortless. Ray is the AI Compute Engine at the center of some of the world's most powerful AI platforms - Our tech is in production at companies like OpenAI, Uber, Spotify, Instacart, and Cruise - We're backed by Andreessen Horowitz, NEA, and Addition, with $250M+ raised to date - Recent partnerships with Azure, CoreWeave, and Google Cloud are putting AI-native compute directly into enterprise environments - Competitive salary and equity, plus health/dental/vision coverage (many plans up to 99% employer-covered) - We offer flexible time off, paid parental leave, and mental health support Anyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1Kt2S6_k_SjxaEdGowH4rngVdg2ApAQV3/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1K3Nz72xgsU2hngnVUEu53wEeZjbAMbnZ/view?usp=sharing
Distributed LLM Inference Engineer
About Anyscale At Anyscale https://www.anyscale.com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=23&pp=iAQB, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=46&pp=iAQB, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date. About the role As a Distributed LLM Inference Engineer, you will help systems and optimizations that push the boundaries of performance for inference at large scale. This is an incredibly critical role to Anyscale as it allows us to achieve a market leading position for AI infrastructure. As part of this role, you will - Iterate very quickly with product teams to ship the end to end solutions for Batch and Online inference at high scale which will be used by open-source Ray users and customers of Anyscale - Work across the stack integrating Ray Data and LLM engine providing optimizations achieving low cost solutions for large scale ML inference - Integrate with Open source software like vLLM, work closely with the community to adopt these techniques in Anyscale solutions, and also contribute improvements to open source - Follow the latest state-of-the-art in the open source and the research community, implementing and extending best practices We'd love to hear from you if you have - Familiarity with running ML inference at large scale with high throughput and low latency - Familiarity with deep learning and deep learning frameworks (e.g. PyTorch) - Solid understanding of distributed systems, ML inference challenges Bonus points! - ML Systems knowledge - Experience using Ray - Work closely with community on LLM engines like vLLM, TensorRT-LLM - Contributions to deep learning frameworks (PyTorch, TensorFlow) - Contributions to deep learning compilers (Triton, TVM, MLIR) - Prior experience working on GPUs / CUDA Compensation At Anyscale, we take a market-based approach to compensation. We are data-driven, transparent, and consistent. As the market data changes over time, the target salary for this role may be adjusted. This role is also eligible to participate in Anyscale's Equity and Benefits offerings, including the following: - Stock Options - Healthcare plans, with premiums covered by Anyscale at 99% for both employees and dependents - 401k Retirement Plan - Education & Wellbeing Stipend - Paid Parental Leave - Fertility Benefits - Paid Time Off - Commute reimbursement - 100% of in-office meals covered Anyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1Kt2S6_k_SjxaEdGowH4rngVdg2ApAQV3/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1K3Nz72xgsU2hngnVUEu53wEeZjbAMbnZ/view?usp=sharing
Software Engineer - ML Infrastructure
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Anduril’s core Lattice platform integrates heterogeneous sensors across large fleets of assets to build a single common operating picture, providing mission-critical situational awareness and context to our users. As a Software Engineer on the ML Infrastructure team, you’ll be responsible for developing the core underlying systems and infrastructure that perception engineers across the business leverage to field sophisticated, autonomous edge systems. ABOUT THE JOB In particular, you’ll develop our core data management systems for perception and autonomy use-cases, including indexing multi-modal sensor data, our model training/orchestration systems (that run across cloud-based and on-premise compute), as well as our internal perception platform. This platform enables dataset querying and discovery, orchestrating an internal suite of frontier models to tag and curate data, and ultimately train & evaluate models and deploy them back to edge devices. This team also owns our core training libraries/SDKs for perception engineers, and partners with them to integrate novel model architectures into our standard infrastructure so the entire company can benefit from each improvement. REQUIRED QUALIFICATIONS - Build & operate our core model training & dataset management infrastructure : Perception engineers rely on reliable, high quality data to continually retrain and improve our detection & tracing systems. You’ll own and operate our core training systems, as well as our dataset management infrastructure. - Improve perception engineer-facing tooling & SDKs: Perception engineers across Anduril rely on our core model training and evaluation framework. You’ll partner closely with these engineers to understand their pain-points & feature requests, and iterate with them directly to deliver them. - Integrate frontier models for data augmentation, curation, and synthetic data generation: While this team is not ultimately responsible for delivering perception systems for particular products, you will be responsible for integrating frontier models at all stages of our dataset curation/annotation pipeline, as well as enabling semantic search across our broad data holdings. - Enable observability & traceability of the full ML lifecycle: You’ll build and develop systems that enable real-time monitoring of training jobs, tools to store/curate model evaluation reports, and our model registry. This integrates with our Fleet Management systems to continuously deliver model improvements to our edge systems. - Eagerly engage with our customers : We partner closely with business line engineering teams to understand their specific data requirements and iteratively deliver solutions that meet their particular needs and constraints. PREFERRED QUALIFICATIONS - Strong proficiency in modern Python & exposure to Rust/the Rust ↔ Python toolchain (PyO3, Maturin, etc). - Experience with modern deep learning frameworks such as Tensorflow or PyTorch, as well as distributed training paradigms - Experience leveraging modern MLOps tooling on the orchestration side (Modal, Flyte, Airflow, Dagster, etc.), as well as common ML observability platforms (Weights & Biases, MLFlow, etc) - Familiarity with building and deploying backend services into a Kubernetes environment - U.S. Person status is required as this position needs to access export controlled data. Eligibility to obtain/maintain a US Top Secret clearance is also desirable. US Salary Range $166,000 - $220,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Senior ML Engineer, Core Development
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise , technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime , 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. About the Team: Air Dominance & Strike designs, builds, and flies autonomous air vehicles—from collaborative combat aircraft to expendable cruise missiles and counter-UAS interceptors. Our vehicles move from whiteboard to first flight on timelines that traditional primes consider impossible, which means our design cycles live or die on how fast we can close the iteration loop. The Anduril AI Engineering team exists to collapse that loop. We are engineers first. We work from engineering first principles and unlock capability through machine learning and AI. We are building to scale across CFD, FEA, thermal, and electromagnetics, with pipelines, architectures, and validation practices that carry across programs. About the Job We are looking for a Machine Learning Engineer to apply the latest research in physics ML to the toughest bottlenecks in our design cycle. This role owns the entire surrogate modeling stack for Air Dominance & Strike—the architectures, the training infrastructure, the simulation data pipelines that feed it, and the tooling design engineers use to consume predictions. You will develop, train, and deploy surrogate models that accelerate the physics simulations underpinning our air vehicle programs. Working alongside aerodynamicists, structures engineers, and thermal engineers, your models will directly inform decisions on hardware that actually flies. Where current methods fall short, you will develop new ones, with ample room to identify novel applications of physics ML across our portfolio. Defense experience is not required. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve. This role is based onsite in our Costa Mesa, CA office. What You'll Do - Own the Surrogate Modeling Stack: Drive the end-to-end design, training, and deployment of production-grade surrogate models to accelerate critical simulation workflows (CFD, FEA, thermal, structural, and aeroelastic) across air vehicle design. - Develop State-of-the-Art Architectures: Design and implement neural architectures tailored to engineering physics, developing new techniques for uncertainty quantification, active learning, and inverse problems (such as geometry and shape optimization). - Build Robust Data & Training Infrastructure: Create the pipelines behind the training—extracting, aggregating, and sanitizing tens of thousands of high-fidelity results from solver outputs. - Optimize & Integrate: Optimize inference for the design loop (maximizing GPU utilization, batched evaluation, and interactive-speed latency) and seamlessly integrate surrogate predictions into the tooling our domain engineers already use. - Collaborate & Mentor: Partner with domain engineers to identify where ML delivers the highest leverage, stay current with Physics AI research, and provide technical mentorship to non ML engineers. Qualifications - Education: BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation. - Experience: 3+ years of experience taking ML models from R&D into production using large-scale scientific or engineering datasets. - Physics ML Expertise: Working knowledge of modern surrogate architectures (e.g. GNNs, Transolver, DoMINO & GeoTransolver) combined with hands-on experience running physical simulations (CFD, FEA, thermal, etc.) and a command of the underlying numerical methods. - Software & Frameworks: Proficiency in Python and MATLAB; experience with PyTorch, TensorFlow, and NVIDIA PhysicsNeMo (Modulus); and experience developing on Linux with GPU accelerators and distributed training. - Data & Engineering Best Practices: Track record of building production data pipelines from heterogeneous engineering sources, utilizing uncertainty quantification, conducting statistical analysis, and building data science dashboards - Clearance: Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance Preferred Qualifications - Advanced Physics ML: Graduate research focused on AI for scientific simulation, experience solving inverse problems (geometry optimization/design under uncertainty), and hands-on experience building active learning or adaptive sampling pipelines. - Domain Expertise: Prior work in aerospace, automotive, turbomachinery, or another simulation-heavy hardware domain, with familiarity in commercial solvers, meshing tools, and CAD interoperability. - Advanced Tooling: Working knowledge of foundational ML methods (Gaussian processes, XGBoost, Elastic Net regression & clustering) with the ability to build custom architectures, advanced skills in visualization software (Plotly, Seaborn, Matplotlib), and ML Ops orchestration experience (e.g. Docker, Weights & Biases, AWS S3, Lambda & SageMaker) Per Anduril's expansion plans, we anticipate this job will be based out of Long Beach, CA beginning late 2027. US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Senior Machine Learning Engineer
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. At Anduril Australia we are developing un-crewed maritime and air domain systems that leverage unsupervised autonomy for the delivery of long endurance, multi-mission capability to our customers. These projects, including but not limited to the Extra Large Autonomous Undersea Vehicle (XL-AUV) Program, require a Senior Machine Learning Engineer with deep experience developing, testing, validating and deploying machine-learning based sensor processing and the technical toolchains they rely on. The Senior Machine Learning Engineer strives to leverage their experience to rapidly architect, design, deliver, support, and evolve next generation autonomous vehicles through the entire product life-cycle. WHAT YOU'LL DO - Lead the design, development, integration and support of machine learning models for perception and data pipelines for a variety of autonomous systems across multiple sensor modalities - Develop software to manage and automate the machine learning lifecycle, including data management, pipelining, labeling and training - Manage model selection, training, validation, evaluation, deployment and regression testing - Manage training data sourcing and integration, and quality - Develop tools and processes for machine learning in a classified environment - Collaborate with cross-functional teams, including software engineers, mechanical engineers, and systems engineers, to ensure effective system integration and testing REQUIRED QUALIFICATIONS - Bachelor’s degree in Robotics, Mechatronics, Computer Science, Engineering, a relevant field, or equivalent experience - Experience in a senior role with the delivery of a production grade software system with a mature machine learning component - Experience with the design, implementation and maintenance of ML Operations, including data acquisition, labeling, data curation, pipeline management, continuous integration, model versioning, and monitoring - Ability to collaborate with data scientists and stakeholders to define and implement robust validation and verification strategies - Experience with the delivery of computer vision capabilities, from the data side as well as the model side - Capacity to work holistically on machine learning enabled capabilities up and down the software stack and through lifecycle through design, implementation, operation and sustainment - Capacity to learn and grow individually, while mentoring junior team members effectively, building team cohesion and capacity - Eligible to obtain and maintain an Australian Government Negative Vetting 2 security clearance (NV2) PREFERRED QUALIFICATIONS - Experience with the delivery of capability with a public sector customer - Experience with workflow management tools such as flyte, airflow, and kubeflow - Experience with deep learning frameworks such as PyTorch - Experience with edge ML applications - Experience with inference engines such as TensorRT - Experience writing C++, Go, Python or Rust - Proficiency in data management and traceability techniques to maintain high-quality datasets and enable reproducibility of experiments and results - Desire to grow in to a Tech-Lead-Manager role, with responsibility for line management of engineers, in addition to delivery Anduril is an equal-opportunity employer, and we encourage candidates from all backgrounds to apply. If you are someone passionate to work on problems that matter, we’d love to hear from you! The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Senior AI Research Engineer
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential. About The Role The AI innovation team at Agility works on building and deploying next-generation robot foundation models and end-to-end policies on humanoid robots. Your goal will be to develop and test cutting-edge methods for imitation learning and reinforcement learning on humanoid robots, in order to establish the techniques necessary for humanoid robots to perform different real-world tasks. You will work on a team, running experiments on humanoid robots, and will research and implement methods which can be transferred into production. About the Work - Design, train, and deploy robust policies for locomotion, manipulation, and dynamic interactions with the environment. - Develop core reinforcement learning infrastructure, including scalable training pipelines and evaluation frameworks. - Design and implement new simulation environments and tasks to support training and deployment of control policies. - Develop, design, and test imitation learning methods - Collaborate with Robotics Software and AI engineering teams to develop policies which can be transferred to production About You - 3+ years of experience developing and deploying learning-from-demonstration - Strong programming skills in Python, with proficiency in deep learning frameworks such as PyTorch. - Experience with modern learning-from-demonstration tools like DiffusionPolicy - Experience with robot data collection, training, and testing on hardware to perform manipulation tasks. - Ability to work collaboratively in a fast-paced environment to deliver safe, high-quality software - MS in Robotics, Computer Science, or a related field. Bonus Points - PhD in Robotics, Computer Science, or a related field. - Publications in top ML or robotics conferences (e.g. NeurIPS, ICML, CoRL, RSS, ICRA). - Familiarity with robot simulation environments (e.g. Mujoco, Isaac Sim) and sim-to-real transfer techniques. - Experience with modern reinforcement learning techniques for locomotion, manipulation, and whole-body control - Experience with writing performant, high quality software in C++ This a hybrid position based out of one of our Salem, Pittsburgh, or Fremont offices. The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future. Anticipated Salary Range $195,000 - $304,000 USD In addition to base pay, our competitive total rewards package consists of the following for full-time employees: - 401(k) Plan: Includes a 6% company match. - Equity: Company stock options. - Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees. - Benefit Start Date: Eligible for benefits on your first day of employment. - Well-Being Support: Employee Assistance Program (EAP). - Time Off: - Exempt Employees: Flexible, unlimited PTO and 12 company holidays, including a winter shutdown. - Non-Exempt Employees: 10 vacation days, paid sick leave, and 12 company holidays, including a winter shutdown, annually. - On-Site Perks: Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations. - Parental Leave: Generous paid parental leave programs. - Work Environment: A culture that supports flexible work arrangements. - Growth Opportunities: Professional development and tuition reimbursement programs. - Relocation Assistance: Provided for eligible roles. - Annual Discretionary Bonus: Provided for eligible roles. All of our roles are U.S.-based. Applicants must have current authorization to work in the United States. Agility Robotics is committed to a work environment in which all individuals are treated with respect and dignity. Each individual has the right to work in a professional atmosphere that promotes equal employment opportunities and prohibits unlawful discriminatory practices, including harassment. Therefore, it is the policy of Agility Robotics to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Agility Robotics prohibits any such discrimination or harassment. Agility Robotics does not accept unsolicited referrals from third-party recruiting agencies. We prioritize direct applicants and encourage all qualified candidates to apply directly through our careers page. If you are represented by a third party, your application may not be considered. To ensure full consideration, please apply directly.
Machine Learning Research Engineer (MLRE) - Workfl...
WHY ACHIRA At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed. At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success. ABOUT THE ROLE We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models. While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities. WHAT YOU’LL DO - Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack. - Rationalize machine learning systems design and software architecture. - Identify blockers and build solutions that scale to the size of foundation models. - Operate as the glue between research scientists and the infrastructure team. ABOUT YOU - At least two years relevant industry experience. - Highly fluent in and enthusiastic about PyTorch and JAX. - Used to thinking in asynchronous primitives. - Strong views on library design: clean abstractions, minimal surface area, consistency. - Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code. - ML generalist who knows what scalable, reliable ML systems look like. NICE TO HAVE Even if you hit none of these bonus features, we encourage you to apply! - Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery. - Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc. - Lack of fear around interacting with quantum chemical scientists and their data pipelines.
AI Residency
About 1X We’re building humanoid robots that work in home - doing the chores, handling the tasks, and giving people their time back. Simple, but it’s not. To do this right, we have to solve robotics, AI, manufacturing - at the same time, at scale, in a form factor that has to be safe enough to live with your family. If you’re inspired by this, you’ll thrive here. We’ve been at this since 2014 and we’re at the point where the hard problems are behind us and the hard work is in front of us. NEO is our flagship - a home robot designed to move, learn, and operate in the real world alongside real people. We’re not demoing it - we’re shipping it. We’re excited to meet you, if this excites you. If you’ve spent your career working on problems that matter and want to see them actually reach the world - this is that moment. We’re scaling, we’re hiring with intention, and we need people who want to build something that will genuinely change how humans spend their time - safely creating abundance for all. About the Program The AI Residency is a fixed-term position (3-6 months) for researchers and engineers who want to work on high-impact AI and robotics problems alongside full-time 1X team members. Residents work on real projects—simulation infrastructure, data management, model evaluation, and capability deployment—that ship to production robots in the field. This is not an internship where you build toy demos: you will contribute meaningfully to the systems that make NEO smarter, and your work will reach hardware in homes and warehouses. Your Charter Accelerate 1X's AI development by applying cutting-edge research to real product problems, whether that means building evaluation infrastructure, scaling data pipelines, improving the data engine, or deploying new capabilities to robots at customer sites. Residents are embedded in the team and treated as full contributors: you will own projects, ship code, and have direct impact on deployed robotic systems within your residency term. Key Outcomes - Apply research-level ideas to production impact translating techniques from the AI and robotics literature into capabilities or infrastructure that ships on real robots - Build or meaningfully improve components of the data engine: front-end tooling for log review and labeling, back-end systems for data cleaning, and evaluation benchmarks for open-source models on robotics tasks - Scale solutions using fleet data contributing to pipelines and systems that improve with more robot experience, not just more hand-labeled data - Deploy at least one new capability or infrastructure improvement to robots at customer sites before the residency term ends Key Competencies - Fast, independent learner picking up new codebases, tools, and problem domains quickly; identifies the highest-leverage contribution and starts making progress without waiting for a detailed spec - Research meets engineering reading a paper and implementing its key ideas, but also writes tested, maintainable code that others can build on; comfortable in both modes - Prototype-to-production instinct moving quickly from idea to working prototype, then cares enough about quality to bring it the rest of the way to something deployable - Collaborative contributor working effectively alongside full-time engineers and researchers, communicates progress and blockers clearly, and contributes to the team's velocity as well as their own projects Minimum Requirements - Bachelor's degree in Computer Science or equivalent; graduate students in ML or robotics strongly preferred - Proficiency with large Python codebases, experience writing tests, and familiarity with deep learning frameworks (PyTorch, TensorFlow, or JAX) - Demonstrated ability to prototype ideas independently and rapidly—a portfolio of projects, research, or open-source work is a strong signal - Experience training large-scale ML models (visual foundation models, LLMs, generative models) or publishing at top ML conferences Preferred Skills - Experience with systems programming and scalable data infrastructure - Familiarity with robotics simulation platforms (Isaac Sim, MuJoCo) or robot middleware (ROS) - Prior research or engineering experience at the intersection of AI and physical systems Benefits & Compensation - $10,000 per month - Health, dental, and vision insurance - 401(k) with company match - Paid time off and holidays Equal Opportunity Employer 1X is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, citizenship, age, marital status, medical condition, genetic information, disability, military or veteran status, or any other characteristic protected under applicable federal, state, or local law.
AI Researcher - Perception
ABOUT 1X We're building humanoid robots that work in home - doing the chores, handling the tasks, and giving people their time back. Simple, but it's not. To do this right, we have to solve robotics, AI, manufacturing - at the same time, at scale, in a form factor that has to be safe enough to live with your family. If you're inspired by this, you'll thrive here. We've been at this since 2014 and we're at the point where the hard problems are behind us and the hard work is in front of us. NEO is our flagship - a home robot designed to move, learn, and operate in the real world alongside real people. We're not demoing it - we're shipping it. We're excited to meet you, if this excites you. If you've spent your career working on problems that matter and want to see them actually reach the world - this is that moment. We're scaling, we're hiring with intention, and we need people who want to build something that will genuinely change how humans spend their time - safely creating abundance for all. ABOUT THE TEAM The Motion team enables NEO to move through and interact with the world. We build the perception NEO needs to understand its surroundings and locomote through any environment, and the control that lets it use its whole body to accomplish real tasks - crawling, bracing, climbing, lifting with more than just its arms. Because NEO operates around people, safe and compliant motion is a design constraint on everything we ship, not a feature layered on top. YOUR CHARTER Own perception for NEO from first prototype to fleet-wide deployment. You will build the vision systems that measure the robot's own geometry, recover human and object pose, and reconstruct the terrain and scenes NEO has to walk through and work in. How precisely NEO places a hand or plans a step is bounded by the perception you deliver, and you will measure your impact on deployed robots rather than on benchmarks. KEY OUTCOMES - Kinematic Calibration: Recover each robot's true geometry from camera data so controls and learned policies act on an accurate model of the body they drive. - 3D Scene Understanding: Deliver depth and scene reconstruction accurate enough to drive terrain-aware locomotion and contact-rich manipulation. - Pose Estimation: Ship human & object pose estimation models that can be used for large scale data labelling efforts and human/robot motion generation models. - Sensor Fusion: Drive time synchronization and fusion across cameras and inertial sensors so every downstream consumer gets spatially and temporally consistent data. - Calibration at Scale: Own intrinsic and extrinsic camera calibration - automated on the production line, monitored for drift across the fleet, and recoverable through field tools. - Evaluation: Establish the datasets and metrics that show whether a perception change improved robot behavior, not just an offline score. KEY COMPETENCIES - Geometric Vision: Fluent in camera models, multi-view geometry, and non-linear optimization; reaches for classical or learned methods based on the problem. - Production ML: Trains and ships modern vision models, including transformer backbones, generative approaches, and learned depth, and makes them fast and predictable on embedded compute. - Hardware Grounding: Treats sensor noise, timing, and calibration drift as first-class problems, and traces how each propagates into a control loop or policy rollout. - Prototype to Production: Stands up a new capability from a paper and a test rig, then makes it repeatable across a fleet and monitored in the field. - Technical Direction: Sets the approach for a problem area, defends tradeoffs across sensing, models, and compute budget, and stays hands-on. MINIMUM REQUIREMENTS - Proven track record of shipping perception systems that run on physical hardware in the real world - robotics experience welcome but not required - Demonstrated ability to train, evaluate, and deploy deep learning models for detection, depth, or pose estimation in PyTorch - Deep experience with geometric computer vision, camera-based kinematic calibration (hand-eye / foot-eye calibration), and camera calibration including intrinsics & extrinsics - Experience owning a capability end to end, from problem definition through deployment and iteration on real failures - Strong Python and modern C++ in large shared codebases, with solid testing and code review practices - 5+ years of relevant industry experience, or an advanced degree plus equivalent hands-on experience PREFERRED SKILLS - Experience with visual-inertial odometry or SLAM, including multi-sensor time synchronization and fusion - Familiarity with robot kinematics and frame transforms, and how calibration error shows up in manipulation and locomotion - Experience building and adapting segmentation and classification models, including fine-tuning open-vocabulary or foundation backbones for a specific domain - Experience reconstructing scenes and terrain from video using differentiable rendering, neural rendering, or Gaussian splatting - Exposure to automating calibration and validation in manufacturing, or to building field diagnostic tools for deployed hardware - Familiarity with sensing beyond RGB such as radar, thermal, or near-infrared COMPENSATION - Salary Range: $200,000 - $300,000 + Equity BENEFITS - Comprehensive medical, dental, and vision coverage - Generous paid time off, company holidays, and parental leave - 401(k) plan with company match (100% on the first 3% of contributions, 50% on the next 2%) - Flexible Spending Accounts (FSA) and Health Savings Accounts (HSA) options - Commuter benefits (transit and parking) - Short-term and long-term disability, and life insurance - Employee Assistance Program (EAP) for mental health, financial, and personal support - Onsite snacks and catered lunches EQUAL OPPORTUNITY EMPLOYER 1X is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, citizenship, age, marital status, medical condition, genetic information, disability, military or veteran status, justice system impact, or any other characteristic protected under applicable federal, state, or local law.
AI Scientist - Biomedical Multimodal Modeling
About Xaira Therapeutics Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London. About the Role We are building foundation models for biological systems, focusing on learning from complex, multimodal data. Our approach combines representation learning and generative modeling to capture structure, variation, and latent organization in high-dimensional settings. We work with both image-based data and high-dimensional molecular (omics) measurements, treating them as complementary views of the same underlying system. Our goal is to develop models that can both encode rich representations and generate or infer missing aspects from partial observations. What You’ll Do - Develop multimodal foundation models spanning image and molecular data - Design approaches that unify representation learning and generative modeling - Train large-scale models on structured, high-dimensional datasets - Explore cross-modal alignment, prediction, and generation - Build scalable systems for data processing, training, and evaluation - Work in a research-driven environment with evolving problem definitions What We’re Looking For Strong background in deep learning, especially in modern architecture - Experience with representation learning (self-supervised, contrastive, masked modeling, etc.) - Experience with generative modeling (diffusion, autoregressive models, VAEs, or related approaches) - Proficiency in Python, ML frameworks, and CI/CD (PyTorch, JAX, etc.) Relevant Experience - Work on multimodal learning or cross-domain representation learning - Experience with image data, molecular / omics-like data, or both - Familiarity with large-scale training Bonus - Exposure to biological or scientific datasets - Experience with paired or partially aligned multimodal data - Comfortable working in open-ended, evolving problem spaces Compensation We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $170,000 - $240,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience. Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end. TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.
AI engineer (UK)
🚀 About WRITER WRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Seattle, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. 📐 About the role As an AI engineer at WRITER, you'll be at the forefront of shaping how enterprises harness superintelligence. This isn't just about theory; you'll be building tangible AI solutions that power the future of work for hundreds of the world's leading companies. Your work will directly impact the performance, scalability, and ethical alignment of our cutting-edge LLMs and AI agents, enabling businesses to unlock unprecedented levels of productivity and innovation with AI that is truly grounded in their data. This role can be hybrid in our London hub. You'll report to the head of AI engineering. 🦸🏻♀️ What you'll do - Architect, develop, and deploy high-performance, scalable AI applications into production environments, ensuring robust integrations with our end-to-end platform. - Drive the development of intelligent agents and AI-powered features, translating complex research into practical, impactful solutions for our customers. - Collaborate closely with research scientists, data scientists, and product managers to define technical requirements and deliver features that address critical business needs. - Contribute to the research and evaluation of emerging AI technologies, frameworks, and tools to maintain WRITER's leadership in the enterprise generative AI space. - Champion responsible AI practices, including bias detection, explainability, and safety alignment, making sure our AI is trustworthy and ethical. ⭐️ What you need - 5+ years of professional experience building and deploying machine learning or AI systems in a production environment. - Proficiency in Python and experience with relevant AI/ML frameworks such as PyTorch, TensorFlow, or JAX. - A strong foundation in machine learning principles, deep learning architectures, and natural language processing, particularly with large language models. - Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps tools for managing the AI lifecycle. - Demonstrated ability to write clean, efficient, and well-tested code for scalable software systems. - Exceptional problem-solving skills and a passion for tackling complex technical challenges with creative AI solutions. - You Connect by collaborating seamlessly across diverse teams, Challenge the status quo to innovate, and Own your projects with a commitment to impact and excellence. 🍩 Benefits & perks (UK full-time employees): - Generous PTO, plus company holidays - Comprehensive medical and dental insurance - Paid parental leave for all parents (16 weeks) - Fertility and family planning support - Early-detection cancer testing through Galleri https://www.galleri.com/partner/writer - Competitive pension scheme and company contribution - Annual work-life stipends for: - Wellness stipend for gym, massage/chiropractor, personal training, etc. - Learning and development stipend - Company-wide off-sites and team off-sites - Competitive compensation and company stock options
AI engineer
🚀 About WRITER WRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Seattle, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. 📐 About the role As an AI engineer at WRITER, you'll be at the forefront of shaping how enterprises harness superintelligence. This isn't just about theory; you'll be building tangible AI solutions that power the future of work for hundreds of the world's leading companies. Your work will directly impact the performance, scalability, and ethical alignment of our cutting-edge LLMs and AI agents, enabling businesses to unlock unprecedented levels of productivity and innovation with AI that is truly grounded in their data. This role can be hybrid in our San Francisco, New York City, or Seattle hubs. You'll report to the head of AI engineering. 🦸🏻♀️ What you'll do - Architect, develop, and deploy high-performance, scalable AI applications into production environments, ensuring robust integrations with our end-to-end platform. - Drive the development of intelligent agents and AI-powered features, translating complex research into practical, impactful solutions for our customers. - Collaborate closely with research scientists, data scientists, and product managers to define technical requirements and deliver features that address critical business needs. - Contribute to the research and evaluation of emerging AI technologies, frameworks, and tools to maintain WRITER's leadership in the enterprise generative AI space. - Champion responsible AI practices, including bias detection, explainability, and safety alignment, making sure our AI is trustworthy and ethical. ⭐️ What you need - 5+ years of professional experience building and deploying machine learning or AI systems in a production environment. - Proficiency in Python and experience with relevant AI/ML frameworks such as PyTorch, TensorFlow, or JAX. - A strong foundation in machine learning principles, deep learning architectures, and natural language processing, particularly with large language models. - Experience with cloud platforms (e.g., AWS, GCP, Azure) and MLOps tools for managing the AI lifecycle. - Demonstrated ability to write clean, efficient, and well-tested code for scalable software systems. - Exceptional problem-solving skills and a passion for tackling complex technical challenges with creative AI solutions. - You Connect by collaborating seamlessly across diverse teams, Challenge the status quo to innovate, and Own your projects with a commitment to impact and excellence. 🍩 Benefits & perks (US Full-time employees) - Generous PTO, plus company holidays - Medical, dental, and vision coverage for you and your family - Paid parental leave for all parents (16 weeks) - Fertility and family planning support - Early-detection cancer testing through Galleri https://www.galleri.com/partner/writer - Flexible spending account and dependent FSA options - Health savings account for eligible plans with company contribution - Annual work-life stipends for: - Wellness stipend for gym, massage/chiropractor, personal training, etc. - Learning and development stipend - Company-wide off-sites and team off-sites - Competitive compensation, company stock options and 401k WRITER is an equal-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice https://writer.com/legal/candidate-privacy-notice/.
Performance Engineer (Inference, Training & GPU)
ABOUT WORLD LABS World Labs https://www.worldlabs.ai/ is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models. Co-founded by Dr. Fei-Fei Li https://profiles.stanford.edu/fei-fei-li, Justin Johnson https://www.linkedin.com/in/justin-johnson-41b43664/ and Ben Mildenhall https://www.linkedin.com/in/ben-mildenhall-86b4739b/, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds. The company’s flagship product, Marble https://marble.worldlabs.ai/, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences. Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment. ROLE OVERVIEW We are looking for a Performance Engineer to make World Labs’ models train and serve as fast as the hardware allows. Running large generative world models at scale is a novel systems problem. You will find the bottlenecks — in kernels, in the serving path, in the training loop, in how we use our GPUs — and eliminate them. Your ownership is technical and concrete: the throughput you unlock, the latency you cut, the utilization you win back, and the correctness you hold while doing it. You will work up and down the stack, from low-level tensor and kernel optimization to fleet-wide serving efficiency, in close partnership with the researchers whose models you are accelerating. This is a hands-on, individual-contributor role. You will profile, design, build, and ship code directly. WHAT YOU WILL DO - Optimize inference and serving end to end — latency, throughput, batching, caching, and scheduling — to serve our models efficiently at production scale. - Write and tune GPU kernels (CUDA, Triton) for hot paths; drive kernel fusion, memory- and bandwidth-bound optimization, and low-precision (FP8/INT8) execution. - Optimize training throughput and GPU utilization: parallelism strategies, communication/compute overlap, mixed precision, and eliminating pipeline stalls. - Build performance models, profiling workflows, and observability that make throughput, latency, cost, utilization, and their tradeoffs legible across the stack. - Own numerical correctness across precision, kernel, and hardware changes — treating correctness as part of performance, not separate from it. - Partner with researchers to productionize models for serving and to make experiments run faster and more reliably. - Where needed, work on the distributed systems that training and inference run on — but the core of the job is squeezing the most out of every GPU. KEY QUALIFICATIONS You should excel at the fundamentals below — we index on inference, serving, GPU optimization, and training performance. Distributed-systems breadth is welcome, but secondary. - Strong performance-engineering foundations: profiling, roofline analysis, latency/throughput optimization, and disciplined root-cause investigation. - Deep GPU programming and optimization experience (CUDA and/or Triton) — kernel-level tuning, memory hierarchy, and bandwidth optimization at scale. - Hands-on experience optimizing inference and serving for large models: batching, KV/prompt caching, quantization, and low-latency, high-throughput sampling. - Hands-on experience optimizing training performance: parallelism, distributed communication, mixed/low precision, and utilization. - Working knowledge of ML framework internals (PyTorch and/or JAX; torch.compile, XLA, or similar compiler paths). - Strong proficiency in Python, with the ability to drop into C++/CUDA (and Rust or Go) as the work demands. - High-ownership mindset — you measure yourself by throughput shipped and latency cut, not tickets closed. PREFERRED QUALIFICATIONS - Experience at an AI lab or ML-native company, optimizing systems used directly by researchers and productionizing research code. - Low-precision and numerics depth: FP8/INT8 quantization, mixed-precision, and detecting numerical regressions across hardware platforms. - Distributed systems for large-scale training and inference — collective communication (NCCL), interconnects (NVLink), model and tensor parallelism, and fault tolerance. A strong plus, but not a substitute for the core skills above. - Experience serving generative, diffusion, video, or 3D/spatial models — not just text LLMs. - Multi-accelerator experience (GPU plus TPU or Trainium) and partnering with hardware vendors on accelerator capabilities. - Building performance-modeling and observability frameworks for GPU utilization and cost. WHO YOU ARE - Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible. - Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger. - Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people. - Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence. We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad. Join us, and let's make history together. Logistics: Location: This role is based in San Francisco, California; we work in-person in the office 5 days a week. Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. Benefits: We offer generous health, dental, vision and 401K benefits, flexible PTO, paid parental leave, and relocation support as needed. Equal Employment Opportunity World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
Senior Machine Learning Engineer, Vision Models
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ ABOUT OUR ENGINEERING TEAMS Wayve’s engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale. ABOUT THE MEASUREMENT TEAM The Measurement team within AI Evaluation builds and qualifies the offline scene-understanding models Wayve uses to measure driving performance after on-road runs and in simulation. Offline modelling provides greater compute, larger models, and access to past and future temporal context, creating unique opportunities to build credible evidence at fleet scale. 🧠 YOUR DAY-TO-DAY - Build, train, and fine-tune scene-understanding models for offline measurement. - Adapt on-vehicle architectures and Wayve foundation models for offline use. - Improve accuracy and generalisation across vehicle platforms, geographies, and driving conditions. - Benchmark your models, diagnose failure modes, and use error analysis to guide iteration. - Define quality bars and statistically credible evidence for validation pipelines and safety cases. - Collaborate with on-vehicle modelling, evaluation, data curation, and simulation teams. - Mentor others and contribute to strong machine-learning and engineering practices. 🧩 WHAT YOU’LL BE WORKING ON - Transformer-based, multimodal, and vision-language models for detection, segmentation, classification, and scene understanding. - Offline models that use higher compute budgets, larger capacity, and bidirectional temporal context. - Automated benchmarks that assess coverage, mine rare events, and measure driving behaviour. - Fleet-scale camera, lidar, and simulation datasets. - Evidence used in model-development decisions, validation pipelines, customer deliverables, and safety cases. 🙌 YOU SHOULD APPLY IF - You have 4+ years in ML engineering and have trained and shipped deep-learning models in production. - You have hands-on experience with modern computer-vision, transformer, multimodal, or VLM architectures. - You have adapted or fine-tuned large pretrained or foundation models and understand data, loss, and representation trade-offs. - You are proficient in Python and PyTorch or a similar framework and comfortable with large-scale training. - You can take ambiguous modelling problems from scoping to a working, measurable solution. - Experience in 3D scene understanding, offline modelling, auto-labelling, temporal or world models, autonomous vehicles, robotics, or distributed training is valuable. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. MORE ABOUT WAYVE 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalise and improve at scale. Our ambition is to make autonomy universal. Wayve’s mapless and hardware-agnostic AI platform integrates with global OEM partners, enabling continuous software evolution from L2+ through to L4. HOW WE WORK 💻 — LOCATIONS & FLEXIBLE WORKING This role is based in Sunnyvale. We operate a hybrid model that combines in-person collaboration with focused time working remotely. 🔍 THE INTERVIEW PROCESS - Initial call / recruiter screen (25 min) - Competency interview (HM Screen; 60 min) - Deep-dive technical interviews (Applied ML, System Design, Programming; 3 hours) - Final interview focused on mission and values alignment (60 min) We’ll always explain the format and work around your availability. WHAT’S IN IT FOR YOU (LOCATION DEPENDENT) - 💰 Salaries benchmarked against the market annually - 📈 Meaningful equity - ✈️ Relocation support and visa sponsorship where applicable - ✅ Hybrid working and access to vehicle workshops and labs - 📚 Learning and development support - 🩺 Comprehensive location-dependent health, family, retirement, and wellbeing benefits A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
Principal Research Scientist, Robot Foundation Mod...
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Science Teams We are looking for a Principal Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team within Wayve Science as a founding member. MEGA is building foundation models for general-purpose robots beyond self-driving vehicles. Our focus is on creating intelligent agents that can perceive, reason, move and manipulate the physical world across diverse embodiments, including mobile manipulators, dual-arm platforms and humanoids. This is a senior, high-impact role with genuine 0-to-1 ownership. You will help define the research agenda, technical strategy and foundations of a new robotics program, working alongside world-class researchers in foundation models, embodied intelligence and large-scale machine learning. You will have the opportunity to work across the entire robot-learning stack: building scalable data flywheels, developing new model architectures and learning algorithms, training large models, designing rigorous evaluations and deploying policies on real robots. Your day-to-day work may span vision-language-action models, world and action models, multimodal and omni models, video generation, reinforcement learning, imitation learning and behavioral cloning. You will collaborate closely with researchers, ML engineers, roboticists and hardware teams to move ambitious ideas rapidly from research hypotheses to large-scale experiments and impressive real-world capabilities. 🧠 Your day-to-day - Research in architectures and data around robot foundation models. - Implement and experiment with different models such as VLAs, WAMs, omni models, and video models. - Implement and experiment with different learning approaches of the RL/BC kind. - Synthesize and filter large video datasets. - Build scalable distributed training pipelines and infrastructure. - Influence and/or own robot policy development and data decisions. 🧩 What you’ll be working on - Lead research into architectures, data and learning approaches for robot foundation models. - Design, implement and evaluate models such as VLAs, WAMs, omni-modal models, video models and related foundation-model architectures for robotics. - Explore and develop learning approaches including reinforcement learning, behavioural cloning and other methods relevant to robot policy development. - Synthesize, curate and filter large-scale video datasets for model training and evaluation. - Build and use scalable distributed training pipelines and infrastructure for large models and large datasets. - Influence and/or own technical decisions around robot policy development, data strategy and model design. - Collaborate closely with scientists, engineers and robotics teams to connect research progress to real-world robot performance. - Communicate research clearly internally and, where appropriate, contribute to external publications and Wayve’s scientific presence. 🙌 You should apply if - Deep experience in machine learning, with a focus on one or more of: vision-language models, video models, robot policies, or foundation models for robotics or embodied AI. - Experience with scalable training, such as multi-node training, large datasets and/or large model training. - Strong research track record, including publications in top-tier venues such as ICRA, CoRL, CVPR, NeurIPS, ICML or ICLR. - Strong coding skills and hands-on experience with modern machine learning frameworks. - Ability to design and drive an independent research agenda while collaborating closely with engineering and robotics teams. - Experience translating research ideas into working systems, experiments or deployed capabilities. - Strong communication skills and the ability to influence technical direction across teams. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency Interviews (Pytorch Debugging and hiring manager interview; 1.5 hours total) - Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total) - Final interview: Mission & values alignment (45 mins).. We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
Machine Learning Engineer, Synthetic Data
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Simulation Teams Simulation is advancing end-to-end autonomous driving research. The team’s mission is to accelerate AV2.0 by incubating capabilities that become company-level advantages — generative world models and the synthetic data they produce are one of those. The goal of this role is to build, scale, and optimise next-generation world model architectures (GAIA and successors) and bridge them into high-throughput generation and training infrastructure, so synthetic data can dramatically accelerate autonomy development. You will post-train world models for new embodiments and behaviours (rig transfer, pose transfer, dashcam restaging), generate multimodal synthetic experience at scale, and land that data in the same training stack we use for real driving. You sit between ML research and engineering: collaborating with scientists on architecture and conditioning, and with platform engineers on generation jobs, training artefacts, and how synthetic data is mixed into training. Your work will decide how fast we can train, evaluate, and deploy driving models on vehicles we have barely collected from. 🧠 Your day-to-day - Model work: Post-train or ablate a GAIA rig-transfer or pose-transfer checkpoint (NVS warp, calibration/intrinsics, shortcut/distillation). Inspect failures: black margins, odometry bias, flickering, wrong curvature column. - Generation at scale: Kick off SDS / Flyte jobs for tens of thousands of segments; debug GPU capacity, KV cache, DDIM step count, MCAP/delta-table correctness. - Landing in training: Binarise synthetic into a corpus-compatible table, wire sampling hooks, train RL and read suite + on-road diffs. - Capability expansion: Pose-transfer ELK/AEB sets, dashcam→Alpha3, or a new OEM rig (Nissan Proto 2.1, Gen3, BMW). - Cross-team: Driving-model owners on mix ratios, closed-loop/eval on whether generated MCAPs are usable as suites. - Paper / research alignment: Video generation, novel-view / camera transfer, flow-matching / distillation, data-centric training — only insofar as it changes a checkpoint or a mix we ship. 🧩 What you’ll be working on - Post-train and iterate GAIA-class world models for synthetic-data capabilities: rig transfer (new camera/vehicle embodiments), pose transfer (rewritten ego trajectories), and related conditioning (geometry, calibration, actions). - Own the generation loop: config → large-scale GPU inference → training-ready artefacts, with clear lineage from the model and settings that produced them. - Land synthetic data in driving-model training (behaviour cloning, reward models, RL): binarisation, mix ratios, quality filters, and experiments that measure suite and on-road impact — including when synthetic should replace scarce real rig data. - Diagnose and fix geometry, calibration, and controllability failures (intrinsics/extrinsics, NVS warps, odometry/curvature, flickering, camera-layout artefacts) that determine whether generated video is training-grade. - Improve throughput and yield: inference optimisations (shortcut, distillation, KV cache, step count), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist. - Expand coverage to new vehicle platforms and safety-critical scenarios (OEM bring-up; Emergency Lane Keeping / Automatic Emergency Braking). - Partner with world-model researchers, infra, and driving-model owners so generation, evaluation, and training stay one system. 🙌 You should apply if - 4+ years in applied ML / research engineering, with a track record of training and shipping neural nets, not only operating data platforms. - Strong Python and PyTorch (or equivalent); comfort with GPU training, debugging, and reading model code. - Hands-on experience with video, generative, or world models (diffusion / flow-matching / autoregressive video, novel-view synthesis, neural rendering, or similar). - Working knowledge of cameras and 3D geometry (multi-camera rigs, intrinsics/extrinsics, warps/reprojection) and why they break generation or downstream training. - Evidence of taking generated or simulated data into a trained downstream model and measuring impact (mix, ablations, failure analysis). - Ability to operate generation or training at real scale (multi-GPU jobs, workflow orchestration, large video artefacts) and to make that path reliable. - Collaborative, experimental working style with researchers and platform engineers; you will own a capability, not a ticket queue. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More about Wayve: 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work 💻- Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. 🔍 The Interview Process: Our process is clear and respectful of your time: - Initial call / recruiter screen (30 mins) - Competency Interviews (Programming and hiring manager interview; 1.5 hours total) - Deep-dive technical interviews (Systems & domain-specific interviews; 3 hours total) - Final interview: Mission & values alignment (45 mins). We’ll always explain the format and work around your availability. What’s in it for you (Location dependant): 💰 Salaries benchmarked against the market annually 📈 Meaningful equity, sharing in the ownership and long term success of Wayve ✈️ Relocation support and visa sponsorship where applicable ✅ Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs 📚 Learning and development budgets with support for training, conferences and growth 🩺 Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more A quick, honest note before you apply. Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one. If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.
Machine Learning Engineer, Driver Refinement & Val...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™— to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Our goal is to improve the Waymo Driver performance by owning the end-to-end driving behavior for various onroad problems clusters. We work with upstream and the downstream teams on developing the ML models and integrating into the Waymo Driver. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. In this hybrid role, you will report to a Technical Lead Manager. You will: - Own tasks in the ML Driver, take responsibility for task performance, create ML methods and recipes to scale and improve tasks. - Analyze behavior of ML systems in real-world application, identify issues and root causes, advise or develop short- and long-term solutions. - Develop methods and recipes for distributed fine-tuning enabling multiple developers to simultaneously improve the model, develop methods and recipes to avoid regression against a production system. - Monitor ML systems in production, develop methods for automatically detecting issues or regressions, develop AI-aided analysis and debugging tooling. - Develop and maintain model evaluation recipes and metrics for measuring and improving performance of pre-trained and fine-tuned models You have: - Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience - 3+ years experience in Machine Learning and/or Computer Vision - Experience with Python or C++ - Experience with ML frameworks like PyTorch or JAX We prefer: - MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline - Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $175,000 - $215,000 USD
AI Enablement Lead
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Business Systems team defines and builds the business IT architecture that supports the commercial operation of Waymo's autonomous fleet. The team stands at the intersection of finance, operations and IT; through creative architecture, expertise and attention to detail, it ensures that Waymo has world-class capabilities across all its business processes. The Data Intelligence team is the strategic engine driving the democratization of AI across Waymo G&A. Operating at the unique intersection of cutting-edge Generative AI and corporate business strategy, we don't just build solutions in isolation we design, architect, and deploy intelligent, agentic systems that transform how Waymo operates. By bridging the gap between complex data and real-world business action, we empower G&A leaders (from Finance and Supply Chain to People Ops and Legal) to automate manual processes, scale operational efficiency, and make high-velocity, data-driven decisions as Waymo enters hyperscale This role follows a hybrid work schedule and reports to Data Intelligence Manager. You will: - Act as a domain authority in AI across G&A, mentoring team members, setting technical standards, and guiding leadership on AI investments and roadmap prioritization. - Develop and prototype solutions by building proof-of-concepts that leverage large language models (LLMs), agent frameworks, and workflow orchestration tools. - Provide technical oversight by reviewing code and prompts, guiding system design decisions, and defining evaluation methodologies and performance benchmarks. - Partner with business units to identify manual bottlenecks in Finance, HR, Sales, Supply Chain and Marketing applying AI to automate complex processes. - Oversee integration and deployment by ensuring seamless connectivity between AI agents and enterprise systems through APIs, data pipelines, and workflow platforms. You have: - BS degree in Computer Science, or a related technical field, or equivalent practical experience - 8-10 years of professional experience across data science, data engineering and business intelligence disciplines. 3+ years of technical experience with a focus on building and shipping AI/ML products - Mandatory hands-on experience using GenAI tools (vibe-coding) to turn prompts into small prototypes in a constrained environment, then use that artifact to validate requirements. - Strong proficiency in Python and experience with modern AI/ML frameworks (e.g. LangChain, Vertex AI Agent Builder, TensorFlow, PyTorch) and cloud platforms - Experience with GCP, Vertex AI, A2A, MCP and ADK frameworks and their application in enterprise systems - Excellent communication skills with the ability to articulate technical AI concepts to non-technical stakeholders We prefer: - Deep expertise integrating AI and LLM solutions securely with major enterprise SaaS platforms (e.g., Salesforce, SAP, Workday, ServiceNow) in a complex, global corporate environment - Experience designing advanced agentic patterns (e.g., multi-agent orchestration, complex RAG architectures, dynamic tool use) for automating multi-step, cross-platform business workflows. - Proven track record of influencing cross-functional leadership, driving technical strategy, and managing organizational change to ensure high user adoption of new AI tools across non-technical business units - Strong understanding of AI governance, enterprise data privacy standards, and security protocols (e.g., RBAC, data loss prevention) when deploying AI applications on internal corporate data - Demonstrated ability to navigate ambiguity and drive projects from conception to launch in a fast-paced environment - Experience acting as a technical multiplier—mentoring team members, setting engineering standards for AI tooling, and guiding leadership on AI investments and roadmap prioritization. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $200,000 - $247,000 USD
Research, Vision Expertise
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Thinking Machines builds multimodal-first. We’re looking for new team members to advance the science of visual perception and multimodal learning. We think about how vision and language interact at scale. We design architectures that fuse pixels and text, build datasets and evaluation methods that test real-world comprehension, and develop representations that let models ground abstract concepts in the physical world. Our goal is to create multimodal systems that support seamless integration into real-world environments. You’ll work at the intersection of visual understanding, multimodal reasoning, and large-scale model training. You’ll help develop the architectures, data, and evaluation tools that teach AI to see, understand, and collaborate. The best candidate is curious about multimodal interfaces, has experience running large scale experiments and is comfortable contributing to complex engineering systems. While we are looking for a person with expertise in multimodality, Thinking Machines Lab operates in a unified fashion and expects new hires to work across modalities as one team. This role blends fundamental research and practical engineering, as we do not distinguish between the two roles internally. You will be expected to write high-performance code and read technical reports. It’s an excellent fit for someone who enjoys both deep theoretical exploration and hands-on experimentation, and who wants to shape the foundations of how AI learns. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Own research projects on training and performance analysis of multimodal AI models. - Curate and build large-scale datasets and evaluation benchmarks to advance vision capabilities. - Work with our data infrastructure engineers, pretraining researchers and engineers, and product team to create frontier multimodal models and the products that leverage them. - Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia. SKILLS AND QUALIFICATIONS Minimum qualifications: - Ability to design, run, and analyze experiments thoughtfully, with demonstrated research judgment and empirical rigor. - Understanding of machine learning fundamentals, large-scale training, and distributed compute environments. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - Research or engineering contributions in visual reasoning, spatial understanding, or multimodal architecture design. - Experience developing evaluation frameworks for multimodal tasks. - Publications or open-source contributions in vision-language modeling, video understanding, or multimodal AI. - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000- $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Research, Mid Training
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE Mid-training is a step between pre-training and post-training, where we take a base model and train it into the foundation for reasoning. This role owns the late-stage training responsibility that shape what our models are fundamentally capable of, including things like synthetic data strategies, the data mix, quality uplift, context extension and capabilities across coding, math, reasoning, and so on. This role blends fundamental research and practical engineering, as we do not distinguish between the two internally. It's an excellent fit for someone comfortable working across the boundary of pre-training and post-training, and who wants to shape what our models can do at their core. What You’ll Do - Own the data. Decide what the model needs to see for each capability and each area of knowledge, then source, curate, and synthesize it. Build the pipelines that filter, deduplicate, verify, and rewrite raw material into training-grade datasets. You'll be responsible for producing data that actually moves the model, and for making sure it reflects how people really use these models, not just clean benchmark-style tasks. - Improve what the model knows. Design and measure interventions that increase knowledge: targeted corpora, synthetic rephrasings and QA over source documents, knowledge-dense mixes. Characterize how knowledge scales with data and when it is retained through post-training versus forgotten. - Instill behaviors and set the prior. Introduce new behaviors during mid-training and measure them the right way: not just whether an eval goes up, but whether post-training becomes easier, more sample-efficient, and more stable as a result. Work closely with the post-training team to decide which behaviors belong in mid-training and which belong in RL. - Build the quality pipeline. Own the automatic filtering and scoring stack: train and calibrate quality classifiers and LLM-based judges, build verifiers for synthetic data, and use them to raise the quality bar of the mix by a lot, not a little. Give the team fine-grained control over data attributes (difficulty, domain, format, style, correctness) and measure the effect of each. - Develop and tune the recipe. Iterate on mid-training recipes: the collection of datasets, training stages, annealing schedules, and hyperparameters. Measure how recipe choices affect metrics, including downstream of post-training. - Iterate on evals. Mid-training involves a never-ending loop of defining a set of evaluations, optimizing them, and then realizing your existing evals don't capture what matters. You'll be responsible for both making numbers go up and making sure the numbers are meaningful. - Debug and understand. While tuning the details of a training configuration, we often observe results that don't quite make sense. You'll be responsible for both getting things to work and developing a deeper understanding we can bring to the next problem. - Scale and explore. Mid-training will involve a combination of scaling existing methodologies and developing new ones. We'll want to both measure how performance scales with dataset size and explore using completely different kinds of training data. SKILLS AND QUALIFICATIONS Required qualifications: - Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications (we encourage you to apply if you meet some but not all of these): - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - Experience building or owning training datasets for large models, including both synthetic data pipelines and real user data (collection, curation, filtering, or mixture design). - Experience with knowledge injection, continued pre-training, or domain adaptation of large models, and with measuring what a model knows. - Experience building model-based data quality systems: quality classifiers, LLM judges, or verification and rewriting pipelines operating at the scale of billions of tokens. - Experience generating synthetic data and understanding its failure modes (diversity collapse, contamination, teacher artifacts). - Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration. LOGISTICS - This is an IC role within Thinking Machines’ Research Team. You will report directly to our Head of Research, and work most closely with our small group of post-training researchers. - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000- $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Research Lead, Tinker, Fine-tuning Science
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE At Thinking Machines we build tools that enable people to make AI their own, customizing models to serve their unique needs. This includes the ability to train model weights. You'll lead the Fine-tuning Science team, setting the research agenda for frontier customization techniques and for Tinker, the leading post-training engine. This is a player-coach role: you'll stay hands-on in the science while growing the team, shaping its direction, and making sure findings ship into Tinker. You'll work closely with our internal research teams, contribute to open science, and engage with external users. WHAT YOU’LL DO In this role, you'll advance the science of fine-tuning and frontier post-training techniques. You’ll: - Set the research agenda: choose the problems, place the bets, and own the roadmap for pushing Tinker quality, efficiency, and reliability to the frontier. - Lead and grow the team: hire, mentor, and develop researchers, and set the bar for experimental rigor and research taste. - Stay hands-on in areas like LoRA and parameter-efficient fine-tuning and how they interact with RL and post-training. - Improve the stability, efficiency, and reliability of large-scale fine-tuning and RL runs on Tinker. - Represent the work externally through papers, technical blog posts, and community contributions. SKILLS AND QUALIFICATIONS Required qualifications: - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - A track record of leading research – setting direction for a team or a major research effort and delivering on it. - Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales. - Clarity in communication: an ability to explain complex technical concepts in writing and to build alignment across science, systems/infra, product. - Strong interest in our mission to enable custom models. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs. - Prior experience with RLHF, RLAIF, preference modeling, or reward learning for large models. - Experience managing or analyzing human data collection campaigns or large-scale annotation workflows. - Research or engineering contributions in alignment, data-centric AI, or human-AI collaboration. - Experience with RL training stability techniques for large runs. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Research Engineer, Infrastructure, Training System...
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an infrastructure research engineer to design and build the core systems that enable scalable, efficient training of large models for deployment and research. Your goal is to make experimentation and training at Thinking Machines fast and reliable to ensure our research teams can focus on science, not system bottlenecks. This role is ideal for someone who blends deep systems and performance expertise with a curiosity for machine learning at scale. You’ll take ownership of the training stack end to end, ensuring every GPU cycle drives scientific progress. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Design, implement, and optimize distributed training systems that scale across thousands of GPUs and nodes for large-scale training workloads. - Develop high-performance optimizations to maximize throughput and efficiency. - Develop reusable frameworks and libraries to improve training reproducibility, reliability, and scalability for new model architectures. - Establish standards for reliability, maintainability, and security, ensuring systems are robust under rapid iteration. - Collaborate with researchers and engineers to build scalable infrastructure. - Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar. - Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Past experience working on distributed training for the world’s largest models to make them stable, reliable, and performant. - Track record of improving research productivity through infrastructure design or process improvements. - Contributions to open-source ML infrastructure such as PyTorch, XLA, Megatron-LM, or DeepSpeed. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
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