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.

Apply using this link. The next cohort of Anthropic fellows starts on July 20, 2026. Apply by May 3, 2026 EOD PST to be considered for this cohort. We will continue accepting applications for later cohorts on a rolling basis. In exceptional circumstances, we may be able to accommodate fellows starting outside of usual cohort timelines.

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, 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

Interview process

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

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).

Fellows workstreams

Due to the success of the Anthropic Fellows for AI Safety Research program, we are now expanding it across teams at Anthropic. We 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 workstreams.

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

AI Safety Fellows
AI Security Fellows
ML Systems & Performance Fellows
Reinforcement Learning Fellows
Economics 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

AI Safety Fellows

Mentors, research areas, & past projects

Fellows will undergo a project selection & mentor matching process. Potential mentors include:

Sam Bowman
Sara Price
Alex Tamkin
Nina Panickssery
Trenton Bricken
Logan Graham
Jascha Sohl-Dickstein
Joe Benton
Fabien Roger
Samuel Marks
Kyle Fish
Ethan Perez

Note: You may research mentors' prior work, but all applications must go through the official form, not the mentors.

Our mentors will lead projects in select AI safety research areas, such as:

Scalable Oversight: Developing techniques to keep highly capable models helpful and honest, even as they surpass human-level intelligence in various domains.
Adversarial Robustness and AI Control: Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
Model Organisms: Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.
Model Internals / Mechanistic Interpretability: Advancing our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.
AI Welfare: Improving our understanding of potential AI welfare and developing related evaluations and mitigations.

On our Alignment Science and Frontier Red Team blogs, you can read about past projects, including:

Subliminal Learning: Language Models Transmit Behavioral Traits via Hidden Signals in Data: Alex Cloud and Minh Le, et al., mentors including Samuel Marks and Owain Evans
Open-source circuits: Michael Hanna and Mateusz Piotrowski with mentorship from Emmanuel Ameisen and Jack Lindsey

For a full list of representative projects for each area, please see these blog posts: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions.

Unique candidate criteria

You might be a particularly great fit for this workstream if you:

Are motivated by reducing catastrophic risks from advanced AI systems
Have experience with empirical ML research projects
Have experience working with large language models
Have experience in one of the research areas mentioned above
Have a track record of open-source contributions

AI Security Fellows

Mentors, research areas, & past projects

Fellows will undergo a project selection & mentor matching process. Potential mentors include:

Nicholas Carlini
Keri Warr
Evyatar Ben Asher
Keane Lucas
Newton Cheng

On our Alignment Science and Frontier Red Team blogs, you can read about some past Fellows projects, including:

AI agents find $4.6M in blockchain smart contract exploits: Winnie Xiao and Cole Killian, mentored by Nicholas Carlini and Alwin Peng
Strengthening Red Teams: A Modular Scaffold for Control Evaluations: Chloe Loughridge et al., mentored by Jon Kutasov and Joe Benton

Unique candidate criteria

You might be a particularly great fit for this workstream if you:

Are motivated by reducing catastrophic risks from advanced AI systems
Have contributed to open-source projects in LLM- or security-adjacent repositories
Have demonstrated success in bringing clarity and ownership to ambiguous technical problems
Have experience with pentesting, vulnerability research, or other offensive security work
Have a demonstrated willingness to do the "dirty work" that produces high-quality outputs
Have reported CVEs or been awarded bug bounties
Have experience with empirical ML research projects
Have experience with deep learning frameworks and experiment management

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

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

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

The Anthropic Institute Fellows (Economics & Policy)

Mentors, research areas, & past projects

Fellows will undergo a project selection & mentor matching process. Potential research areas and mentors include:

Economics
- Maxim Massenkoff
- Peter McCrory
Policy, Security, and Society
- Jack Clark
- Marina Favaro
- Jim Baker

Projects in this workstream may include:

Designing and conducting empirical research on AI's economic effects, drawing on external data sources
Developing new methodological approaches for studying AI's impact on labor markets, the future of work, and society
Analysing the offense–defense balance for AI-enabled cyber and bio capabilities as models scale
Measuring the extent to which model performance increases with custom harnesses?
Identifying market driven mechanisms that could improve societal resilience to anticipated threats from AI systems?
Identifying which metrics relating to AI R&D could service as early warning signals for recursive self-improvement

For past project examples, see:

How AI Impacts Skill Formation: Judy Shen and Alex Tamkin
Stress-Testing Model Specs Reveals Character Differences among Language Models: Jifan Zhang, Henry Sleight, Andi Peng, John Schulman, and Esin Durmus

Unique candidate criteria

You might be a particularly great fit for this workstream if you:

Have an interest in economics or policy research; prior experience in this area is a plus but not required
Are adaptable and collaborative, able to take direction and contribute to team priorities rather than needing to pursue a predetermined research agenda
Are skilled at writing up and communicating your results, even when they're null or unexpected
Are passionate about translating research insights into actionable recommendations for improving AI systems and informing policy

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 London and Berkeley where fellows will work from and mentors will visit. We are also open to remote fellows in the UK, US, or Canada. We will ask you about your availability to work from Berkeley or London (full- or part-time) during the program.

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.

Applications and interviews are managed by Constellation, our official recruiting partner for this program. Constellation also runs the Berkeley workspace that hosts fellows. Clicking "Apply here" will redirect you to Constellation's application portal. You can expect to receive emails from Constellation with application updates.

Apply here

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 research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Come work with us!

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process

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