Reinforcement Learning Fellow - 4-Month Research Mentorship

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic Fellows Program overview The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent – regardless of previous experience. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis. This application is for cohorts starting in July 2026 and beyond. What to expect 4 months of full‑time research Direct mentorship from Anthropic researchers Access to a shared workspace (in either Berkeley, California or London, United Kingdom) Connection to the broader AI safety and security research community Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (varies by country) Funding for compute (≈15k USD per month) and other research expenses Interview process The interview process will include an initial application and reference check, technical assessments and interviews, and a research discussion. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification listed. Research shows that people who identify as being from underrepresented groups are more prone to imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you’re interested in this work. Compensation The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (possible extension). Fellows workstreams Due to the success of the Anthropic Fellows for AI Safety Research program, we are expanding it across teams at Anthropic. We expect a significant overlap in the types of skills and responsibilities across the roles and will by default consider candidates for all the workstreams. AI Safety Fellows AI Security Fellows ML Systems & Performance Fellows Reinforcement Learning Fellows Economics & Societal Impacts Fellows Across the workstreams, you may be a good fit if you: Are motivated by making sure AI is safe and beneficial for society as a whole Are excited to transition into empirical AI research and would be interested in a full‑time role at Anthropic Have a strong technical background in computer science, mathematics, or physics Thrive in fast‑paced, collaborative environments Can implement ideas quickly and communicate clearly Strong candidates may also have: Strong background in a discipline relevant to a specific Fellows workstream (e.g. economics, social sciences, or cybersecurity) Experience in areas of research or engineering related to their workstream Candidates must be: Fluent in Python programming Available to work full‑time on the Fellows program Reinforcement Learning Fellows Fellows will undergo a project selection and mentor matching process. Potential research areas and mentors include: Ruhua Jiang Kaidi Cao Sunny Duan David Brandfonbrener Colt Steele Dino Distefano Will Williams Projects in this workstream may include: Building model‑based tools to better understand AI training data and improve training data quality A research project to better understand generalization Creating RL environments to improve Claude models within your domain of expertise Building RL environments for safety‑related tasks Conducting research and implementing solutions in areas such as RL algorithms Unique candidate criteria You might be a particularly great fit for this workstream if you: Have strong software engineering skills with experience building complex ML systems Can balance research exploration with engineering rigor and operational reliability Enjoy collaborating across research and engineering disciplines Are comfortable working with large‑scale distributed systems and high‑performance computing Have experience with training, fine‑tuning, or evaluating large language models Are adept at analyzing and debugging model training processes Logistics To participate in the Fellows program, you must have work authorization in the United States, United Kingdom, or Canada and be located in that country during the program. We have designated shared workspaces in London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the United Kingdom, United States, or Canada. We will ask you about your availability to work from Berkeley or London (full‑ or part‑time) during the program. We are not currently able to sponsor visas for fellows. To participate, you need to have or independently obtain full‑time work authorization in the United Kingdom, United States, or Canada. The program runs for 4 months, full‑time. If you can’t commit to the full duration, please still apply and note your constraints in the application. We review these requests on a case‑by‑case basis. Please note: We do not guarantee that we will make any full‑time offers to fellows. However, strong performance during the program may indicate that a Fellow would be a good fit for full‑time roles at Anthropic. In previous cohorts, 25–50% of fellows received a full‑time offer, and we’ve supported many more to go on to do great work on AI safety and security at other organizations. We encourage diverse applicants We encourage you to apply even if you do not believe you meet every single qualification. We value representation and strive to include a range of diverse perspectives on our team. #J-18808-Ljbffr Nerdleveltech