Anthropic Fellows Program: ML Systems & Reinforcement Learning
Headquarters: London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA
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.
We are accepting applications on a rolling basis for the next cohort expected to start in January 2027. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines—please note in your application if the January 2027 start date doesn't work for you.
What to Expect
- 4 months of full-time research
- Direct mentorship from Anthropic researchers
- Access to a shared workspace
- Connection to the broader AI safety and security research community
- Weekly stipend: 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country)
- Funding for compute (~$15k/month) and other research expenses
Interview Process
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. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome; we urge you not to exclude yourself prematurely. We strive to include a range of diverse perspectives on our team.
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 (with possible extension).
ML Systems & Performance Fellows
Mentors & Research Areas
Fellows will undergo a project selection & mentor matching process. Potential mentors include:
- Alwin Peng
- Zygi Straznickas
Note: You may research mentors' prior work, but all applications must go through the official form.
Past Project Example: AI agents find $4.6M in blockchain smart contract exploits
Potential Projects:
- Building a CPU simulator for accelerator workloads
- Adding backends for different accelerators on an open-source project
- Building on-demand infrastructure for other infrastructure-heavy fellows projects
- Building complex synthetic data or environment pipelines
Candidate Criteria
You might be a great fit 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
Reinforcement Learning Fellows
Mentors & Research Areas
Potential research areas and mentors include:
- Ruhua Jiang
- Kaidi Cao
- Sunny Duan
- David Brandfonbrener
- Colt Steele
- Dino Distefano
- Will Williams
Potential Projects:
- 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 within your domain of expertise
- Building RL environments for safety-related tasks
- Conducting research and implementing solutions in areas such as RL algorithms
Candidate Criteria
You might be a great fit 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
General Requirements
- Motivation: You are motivated by making sure AI is safe and beneficial for society as a whole.
- Career Goals: You are excited to transition into empirical AI research and would be interested in a full-time role at Anthropic.
- Technical Background: Strong background in computer science, mathematics, or physics.
- Skills: Fluent in Python programming; can implement ideas quickly and communicate clearly.
- Availability: Must be available to work full-time for the 4-month duration.
Logistics
- Work Authorization: You must have work authorization in the US, UK, or Canada and be located in that country during the program.
- Visa Sponsorship: We are not currently able to sponsor visas for fellows.
- Workspace: We have designated shared workspaces in SF. We are also open to remote fellows in the UK, US, or Canada.
- Full-Time Offers: We do not guarantee full-time offers. However, in previous cohorts, 25-50% of fellows received a full-time offer.
Important Note on Scams
Your safety matters. Anthropic recruiters only contact you from @anthropic.com email addresses. We will never ask for money, fees, or banking information before your first day. If unsure, visit anthropic.com/careers directly.
How to Apply
Learn about our policy for using AI in our application process.