Fellows Program, Reinforcement Learning

Anthropic Fellows Program, Reinforcement Learning

London, UK;
Ontario, CAN;
Remote-Friendly, United States;
San Francisco, CA

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

We are accepting applications on a rolling basis for the next cohort of Anthropic Fellows, which is expected to start in late September. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the September start date doesn't work for you.

This page is specific to one of the Anthropic Fellows Work streams, see also the main Anthropic Fellows posting.

• 4 months of full-time research
• Direct mentorship from Anthropic researchers
• Access to a shared workspace (in either Berkeley, California or London, UK)
• Connection to the broader AI safety and security research community
• Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country)
• Funding for compute (~$15k/month) and other research expenses

The interview process will include an initial application & reference check, technical assessments & interviews, and a research discussion.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

The expected base stipend for this role is 3,850 USD / 2,310 GBP / 4,300 CAD per week, with an expectation of 40 hours per week for 4 months (with possible extension).

Due to the success of the Anthropic Fellows for AI Safety Research program, we are now expanding it across teams  expect there to be significant overlap in the types of skills and responsibilities across the roles and will by default consider candidates for all the work streams.

Some of the work streams may include unique assessment steps; we therefore ask you for workstream preferences in the application. You can see an overview of the current work streams below:

• AI Safety Fellows
• AI Security Fellows
• ML Systems & Performance Fellows
• Reinforcement Learning Fellows
• Economics & Societal Impacts Fellows

This page is specific to one of the Anthropic Fellows Work streams, see also the main Anthropic Fellows posting.

• 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 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

• Fluent in Python programming
• Available to work full-time on the Fellows program

Fellows will undergo a project selection & 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 at capabilities that are within your domain of expertise
• Building RL environments for safety-related tasks
• Conducting research and implementing solutions in areas such as RL algorithms

• 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

To participate in the Fellows program, you must have work…