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 (e.g. in trading)
• Have experience with training, fine-tuning, or evaluating large language models
• Are adept at analyzing and debugging model training processes

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

Candidates must be

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

Reinforcement Learning Fellows

Mentors, research areas, & past projects

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

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

Logistics Requirements:

To participate in the Fellows program, you must have work authorization in the US, UK, or Canada and be located in that country during the program.

Workspace Locations:

We have designated shared workspaces in SF. We are also open to remote fellows in the UK, US, or Canada.

Visa Sponsorship:

We are not currently able to sponsor visas for fellows. To participate in the Fellows program, you need to have or independently obtain full-time work authorization in the UK, the US, or Canada.

Program Duration:

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.

The below are Anthropic's policies for full time roles. These do NOT apply to the Fellows Program.

Logistics

• Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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.

Your safety matters to us.

To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How We're Different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent