AI Research Engineer Jobs

Baseten

Baseten powers mission-critical inference for leading AI companies by uniting applied AI research, flexible infrastructure, and seamless developer tooling. We enable companies at the frontier of AI to bring cutting-edge models into production. This role focuses on the applied end of post-training research, working directly with stakeholders from fast-growing AI companies to post-train open-source models that outperform frontier closed models on specialized tasks. The day-to-day involves extracting signal from complex, domain-specific datasets and building reward functions, environments, eval harnesses, and training pipelines to improve models that ship to production and reach millions of users.

$200k - $275k

Datadog

Datadog AI Research (DAIR) is seeking a Staff Research Scientist to lead research in post-training and autonomous agents. This hands-on role involves advancing reinforcement learning, simulated environments, synthetic data generation, and evaluation methods for agents operating in complex systems. You will define the technical direction for research programs, elevate the skills of your colleagues, and collaborate with product and engineering teams to integrate research breakthroughs into Datadog's offerings.

$320k - $400k

Datadog

As a Staff Research Scientist within Datadog AI Research (DAIR), you will drive research in foundation models and world models as a hands-on individual contributor. You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products.

$320k - $400k

Invisible Technologies is seeking a Research Engineer to join its research organization, focusing on reinforcement learning, evaluation methodology, benchmarks, and RL environments for frontier labs and enterprise clients. This role involves defining how frontier models are measured, with direct influence over the methodology and its implementation. You will translate research questions into practical scoring frameworks, evaluation architectures, or environment designs, and then build the production systems to run them. This is a hands-on role for someone who wants to design the approach and write the code to prove it out, with reporting lines varying by experience level.

Salary not listed

Mercor is seeking a Research Scientist Intern to contribute to cutting-edge research in AI. This role focuses on post-training techniques, reinforcement learning with verifiable rewards (RLVR), data generation, and model evaluation. You will investigate how different datasets, rewards, and training methods influence the capabilities and behavior of large language models. The internship involves designing experiments, developing new evaluation methodologies, analyzing model failures, and improving aspects like tool use, agentic behavior, and real-world reasoning. You will collaborate with researchers and domain experts, with the potential for your work to support publications and the development of advanced AI systems.

Salary not listed

HUD is building reliable, fair, and open infrastructure for AI data, aiming to make data valuable for creators and trustworthy for AI labs. As a Research Engineer on the Robotics team, you will develop datasets and evaluations crucial for training and assessing embodied AI systems. This role involves translating research needs into data specifications, creating methods for data validation, and conducting experiments to understand how data characteristics impact model performance. You will collaborate with internal research and engineering teams, and potentially with data vendors and buyers, to enhance robotics data offerings.

$100k - $230k

Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role is for a founding member of the Multi-Embodiment Generalist Agent (MEGA) team, focused on building foundation models for general-purpose robots. These models will learn from large-scale video, language, and robot-interaction data to generalize across tasks and embodiments, enabling agents to perceive, reason about, and act reliably in the physical world. The work combines frontier ML research with direct real-world evaluation on a growing fleet of robots.

$311k - $420k

Percepta

Percepta is transforming critical institutions with applied AI, embedding with industry-leading customers to drive AI transformation. We bring together forward-deployed expertise in engineering, product, and research, along with our in-house toolkit for rapidly deploying agentic workflows and strategic partnerships. As a Research Scientist - Reinforcement Learning, you will work at the intersection of RL research and real-world deployment, advancing decision-making capabilities for critical industries. You will collaborate closely with Embedded Product Managers and engineers to ensure our solutions transform how companies operate.

$200k - $400k

Wayve is seeking a founding Research Scientist to join the Multi-Embodiment Generalist Agent (MEGA) team. This role focuses on building foundation models for general-purpose robots beyond self-driving vehicles, creating intelligent agents capable of perceiving, reasoning, moving, and manipulating the physical world across diverse embodiments. You will play a senior, high-impact role in defining the research agenda, technical strategy, and foundations of a new robotics program. The work involves the entire robot-learning stack, from scalable data flywheels and new model architectures to training large models, designing evaluations, and deploying policies on real robots. You will collaborate with researchers, ML engineers, and roboticists to advance ambitious ideas from research hypotheses to large-scale experiments and real-world capabilities.

$370k - $513k

Exa is an applied AI lab building a novel search engine. We focus on massive-scale infrastructure for web crawling, training state-of-the-art embedding models, and designing high-performance vector databases. Our goal is to create perfect search over all the world's information, surpassing current capabilities. This role is critical to our search architecture, with flexibility in project assignments based on individual skills and interests. We are seeking a research engineer to tackle the complex challenges of understanding web page content, including parsing, classification, credibility assessment, and misinformation detection, all at a web scale.

$180k - $350k

Anthropic

Reinforcement learning (RL) is crucial for Claude's ability to reason, write code, and act autonomously over long horizons. This role is part of the team responsible for building and maintaining the RL training system, which is used to train production models and for researchers across Anthropic to run their experiments. The team collaborates closely with research teams company-wide on the science and engineering required to scale RL effectively. As a Research Engineer, you will be at the heart of RL at Anthropic, with a direct view of RL training at the frontier, as the system you own supports both production and research runs. You will work with collaborators across research teams to understand system performance, identify areas of fragility, and drive future improvements.

$500k - $850k

Datadog

Datadog AI Research (DAIR) is seeking a Research Manager to lead a team focused on fundamental research in foundation models and world models. This role involves advancing large-scale pre-training and multimodal learning using diverse signals from distributed systems. The manager will provide technical leadership, mentor researchers and engineers, shape research direction, and collaborate with product and engineering teams to translate research into products. This position is ideal for an experienced researcher or technical leader who wants to maintain hands-on research involvement while leading a high-performing team.

Salary not listed

Handshake is seeking a Member of Technical Staff, Evals to help define how frontier AI systems are measured, understood, and improved. This is a high-ownership role for researchers who build, partnering with AI researchers, domain experts, and customers to develop new benchmarks, reward and verifier systems, agent-evaluation methodologies, and data-quality techniques. Early members of the team will have significant influence over technical direction, operating culture, and the open-source software, benchmarks, and research products built. The company prioritizes demonstrated research capability, technical judgment, and a builder's mindset over specific titles, degrees, or career paths.

$200k - $350k

Anthropic

Anthropic is dedicated to building AI systems that are safe, reliable, and steerable, aiming to benefit both users and society. This role is central to advancing Reinforcement Learning (RL), the core technology enabling Claude's reasoning, coding, and autonomous capabilities. The RL Scaling team focuses on how RL performance, stability, and learning efficiency evolve with increasing model size, episode length, and compute power. You will be instrumental in developing and scaling next-generation RL algorithms and architectures, from initial research to large-scale deployment, while deeply understanding and addressing the challenges that arise at scale. This position bridges research and engineering, requiring you to build the systems that accelerate iteration and experimentation, and to tackle complex, unsolved problems in Anthropic's largest RL runs.

$500k - $850k

Anthropic

Reinforcement learning is crucial for training AI models to reason, write code, and act autonomously. At scale, this process involves a demanding distributed system where training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts. This system must continuously exchange data and maintain progress despite hardware failures, shifting loads, and evolving research. The efficiency of this system directly impacts learning outcomes and the speed of research iteration. As a Research Engineer on the Distributed Systems team within RL Engineering, you will focus on the most critical bottlenecks within this system. This could involve areas like scheduling, data movement, sandboxed environment execution, storage, networking, fault tolerance, autoscaling, or observability. The role seeks generalist engineers capable of reasoning from first principles across different system layers and prioritizing impactful problems over familiar ones. The ideal candidate will have experience building and operating large distributed systems, a commitment to detail, and a desire to apply their expertise to a complex, heterogeneous, and rapidly changing workload.

$500k - $850k

Datadog

Datadog AI Research (DAIR) is seeking a Research Scientist to partner with Research Engineers on fundamental research problems. The role involves translating research advances into products by collaborating with Datadog's product and engineering teams. The team focuses on high-risk, high-reward problems in cloud observability and security, building on existing AI-powered solutions. The research areas include developing multimodal foundation models for understanding distributed systems and training autonomous agents for observability tasks like incident response and code repair.

$320k - $400k

SecureBio is seeking a Senior Research Scientist to join their AI Biosecurity Assessments team. In this role, you will evaluate frontier AI models for dual-use biological risks before their public release. This involves running technical assessment pipelines, analyzing model behavior, and producing reports that inform relevant audiences about identified risks. Your work will contribute to setting industry safety standards and influencing policy bodies like the NIST US AI Safety Consortium and the European AI Office. You will collaborate with AI firms, internal research teams, and policy makers to ensure the safe development and deployment of advanced AI technologies.

$235k - $300k

Datadog

Datadog AI Research (DAIR) is seeking a Research Engineer to partner with Scientists in transforming research concepts into functional systems. This role involves building the necessary data, tooling, and infrastructure to support rapid iteration, reliable evaluation, and a seamless transition from prototype to production. The team focuses on high-impact problems in cloud observability and security, specifically in training multimodal foundation models for understanding distributed systems dynamics and post-training models to operate autonomously within Datadog's domain, starting with SRE incident response.

Salary not listed

About HUD HUD's mission is to build reliable, fair and open infrastructure for AI data. We want data to be valuable for the people who create it and trustworthy for the labs that train on it. Our team is a quickly growing group of researchers, engineers and operators building the economy that shapes what AI will become. Backed by $16M from top VCs and YC (W25), our marketplace and platform are used by startups, Fortune 500 companies and frontier labs. About the role We’re looking for a Research Engineer for our Robotics team to develop the datasets and evals that make robotics data useful for training and evaluating embodied AI systems. You’ll translate open-ended research needs into data specifications, build methods to validate data, and run experiments to understand which data and structures improve model performance. Responsibilities - Research the data needs of robot learning and physical AI systems, and turn them into concrete dataset and evaluation specifications - Define data schemas, annotations, ground truth, and quality standards across robotics data types - Design collection and review protocols that external data providers can execute reliably - Build tools and validation workflows to audit datasets, identify quality issues, and give providers actionable feedback - Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance - Work with HUD’s research and engineering teams, and when needed with data vendors and buyers, to improve robotics data offerings Experience You may be a good fit if you have: - Experience in robotics, robot learning, embodied AI, or closely related multimodal research - Proficiency in Python and experience building data processing, analysis, or evaluation tools - Experience turning research questions into dataset specifications, experiments, and measurable quality criteria - Strong understanding of what makes robotics data useful for training or evaluation—and where it can be misleading - Attention to detail and the ability to spot subtle errors, coverage gaps, and failure modes in complex data - Experience building research tools or pipelines without a fully prescribed roadmap Strong candidates may also have: - Worked with robot trajectories, demonstrations, video, sensor data, simulation, or other multimodal robotics datasets - Experience with imitation learning, reinforcement learning, or vision-language-action models - Worked in unstructured problem spaces and take ownership from early research through production deployment - Early-stage startup experience and strong communication skills for collaboration across teams and time zones We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria. Team & company details - Team Size: ~25 people currently, mostly full-time in-person, but some remote. - Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc. - Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand. Logistics - Employment: Full-time. - Location: We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones. - Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore. - Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial. What we offer - Competitive compensation - 100% covered top-of-the-line medical, dental, and vision (US and Singapore employees) - Lunch and dinner for in-office employees - Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays - Other perks including an Equinox membership, 401k, and commuter benefits (US employees) and a health/wellness stipend (Singapore employees) - Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is. - Two top-floor offices in San Francisco’s Union Square and Singapore’s Raffles Place - Annual travel budget to visit either office Compensation Actual offers are adjusted for experience and location, but our base salary bands are - San Francisco (and other major US cities): $135,000 - $230,000 - Singapore: $100,000 - $175,000 - Rest of world: $100,000 - $175,000 Due to high volume, we may not actively respond to every application, but feel free to contact us at [email protected] or elsewhere if we missed your application!

$100k - $230k

FAR.AI is a non-profit AI research institute dedicated to ensuring advanced AI is safe and beneficial. The Applied White-Box Methods team focuses on developing, evaluating, and demonstrating techniques that utilize model internals to enhance AI system safety. This involves exploring applications like white-box control, evaluation awareness, and shaping training dynamics for improved alignment. The team bridges the gap between exploratory research and practical deployment by rigorously testing white-box methods on real-world tasks, such as long-context agentic coding, and using feedback to refine these methods for frontier-scale deployment. The scope encompasses any method using model internals for understanding, predicting, or intervening in model behavior, including activation explainers, steering, patching, influence functions, and singular learning theory. These methods are evaluated against strong black-box baselines to ensure their practical value.

$150k - $250k

Tavus

Tavus is building the human layer of AI, aiming to make human-AI interaction as natural as face-to-face communication. We pioneer research in multimodal AI for modeling human communication (language, audio, video) and generating audio-visual avatar behavior, powering applications from text-to-video avatars to real-time conversational video. We are a Series B company backed by top investors, seeking individuals who thrive in startup environments, can prioritize independently, and are willing to take calculated risks to help pave the path forward.

Salary not listed

Anthropic

Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. Decisions in production training depend on knowing how the model is really behaving. This role's mission is to have the best observability into production post-training runs at Anthropic, providing the clearest, most trusted view of model quality during training. You will shape what gets measured, keep the signal trustworthy and timely, and investigate surprising results to inform post-training and release decisions.

$500k - $850k

HUD is building reliable, fair, and open infrastructure for AI data, aiming to make data valuable for creators and trustworthy for AI trainers. We are a growing team of researchers, engineers, and operators backed by significant VC funding. We are seeking a Research Manager to lead research focused on enhancing HUD's agent training data and evaluations for frontier models. This role involves guiding research engineers through complex projects, translating findings into scalable methods, and staying technically engaged while ensuring rigorous experimentation and impactful results. The ideal candidate will possess a deep understanding of model training dynamics, including how signals, data, and evaluation design influence outcomes.

$120k - $295k

Eventual is building data curation for Physical AI, processing petabyte-scale video and sensor data to extract signals about how hands and objects interact, temporal events, and clip reliability for training frontier models. We are a small, well-backed team from leading tech companies, aiming to revolutionize how Physical AI models are trained by providing reliable, curated datasets derived from vast amounts of video footage. This role is based in our SF Mission District office, with a 4-day work week. As Technical Lead, Multimodal Research, you will drive the technical vision for Eventual's understanding of video data. You will own the strategy for selecting, prototyping, and deploying models and representations at scale, ensuring they meet rigorous evaluation standards and cost-efficiency targets. This is a senior individual contributor role focused on setting research direction and making key architectural decisions, while remaining hands-on with research and experimentation.

$150k - $300k