Anthropic Fellows Program, ML Systems & Reinforcement Learning

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.

Apply at the bottom of this page. 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 of 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, assessments, and interviews. We may ask for references later in the process as well.

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.

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, & past projects

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, not the mentors.

For a past example of an engineering-heavy project, see:

AI agents find $4.6M in blockchain smart contract exploits

Projects in this workstream may include:

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

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 d