Applications are Open for the Anthropic Fellows Program 2026 (Fully Funded AI Research Fellowship)

Application Deadline: April 26, 2026 (for July 20, 2026 cohort; rolling thereafter)

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 is a public benefit corporation headquartered in San Francisco, with additional offices in London, UK, and Ontario, Canada. The company was founded by former OpenAI researchers who share a deep commitment to ensuring that artificial general intelligence (AGI) is developed safely and benefits all of humanity. Anthropic is best known for creating Claude, a family of large language models designed to be helpful, honest, and harmless.

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.

Next Cohort Start Date: July 20, 2026

Apply by: April 26, 2026 to be considered for the July 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.

Why This Fellowship Matters

The Anthropic Fellows Program represents a unique opportunity to contribute to cutting-edge AI safety research at a critical moment in the development of artificial intelligence. As AI systems become more powerful and pervasive, ensuring they remain safe, interpretable, and aligned with human values is one of the most important technical challenges of our time. Fellows work alongside world-class researchers, gain hands-on experience with state-of-the-art models, and produce research that can shape the direction of the field. The program’s track record speaks for itself: in one of our earlier cohorts, over 80% of fellows produced papers, and 25-50% of fellows received full-time offers at Anthropic. Whether you come from a background in computer science, mathematics, physics, economics, or cybersecurity, if you are passionate about making AI safe and beneficial for society, the Anthropic Fellows Program could be your entry point into the field of AI safety research.

What to Expect

• 4 months of full-time research – Dedicated time to focus on an empirical AI research project
• Direct mentorship from Anthropic researchers – Work closely with experienced researchers in your chosen workstream
• Access to a shared workspace – In either Berkeley, California or London, UK (remote options also available for UK, US, or Canada)
• Connection to the broader AI safety and security research community – Network with leading researchers and fellow fellows
• Weekly stipend – $3,850 USD / £2,310 GBP / $4,300 CAD (varies by country) plus benefits
• Funding for compute (~$15k/month) and other research expenses

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.

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.

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.

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:

1. AI Safety Fellows
2. AI Security Fellows
3. ML Systems & Performance Fellows
4. Reinforcement Learning Fellows
5. Economics & Societal Impacts 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

Workstream 1: AI Safety Fellows

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

Research Areas:

• 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

Past Projects:

• 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

Unique Candidate Criteria – You might be a particularly great fit 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

Workstream 2: AI Security Fellows

Mentors: Nicholas Carlini, Keri Warr, Evyatar Ben Asher, Keane Lucas, Newton Cheng

Past Projects:

• 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 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

Workstream 3: ML Systems & Performance Fellows

Mentors: Alwin Peng, Zygi Straznickas

Past Example of an Engineering-Heavy Project:

• 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 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

Workstream 4: Reinforcement Learning Fellows

Mentors: 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 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

Workstream 5: Economics & Societal Impacts Fellows

Mentors (Economics): Maxim Massenkoff, Peter McCrory
Mentors (Societal Impacts Research): Judy Shen, Saffron Huang, Kunal Handa

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, future of work, and society
• Analyzing usage patterns of Claude and AI systems more broadly
• Understanding AI reliance and human-AI collaboration through studies
• Evaluating models for their role in human well-being
• Designing model interventions that promote social cohesion around contentious topics

Past Project Examples:

• 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 if you:

• Have an interest in economics or societal impact 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

Interview Process

The interview process will include:

1. Initial application & reference check
2. Technical assessments & interviews
3. Research discussion

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.

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.

Compensation

The expected base stipend for this role is:

• $3,850 USD per week (for fellows in the United States)
• £2,310 GBP per week (for fellows in the United Kingdom)
• $4,300 CAD per week (for fellows in Canada)

This is based on an expectation of 40 hours per week for 4 months (with possible extension). Benefits vary by country.

Funding for compute (~$15,000 per month) and other research expenses is provided separately.

How to Apply

Apply here: Click the application link on the Anthropic careers page or the official Fellows Program page.

Note: 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.

Application link: Click here

Important Notes

Candidate AI Usage Policy: Learn about Anthropic’s policy for using AI in the application process on their careers page.

Encouragement to Apply: 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. This makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Recruitment Scam Warning: 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.

For More Information: Visit the Official Anthropic Careers Page