The Anthropic Fellows program offers funding and mentorship for engineers and researchers to pursue high-priority AI safety research. Fellows work for four months on empirical research questions, aiming to produce public outputs like papers, with close collaboration from Anthropic mentors. The program focuses on areas such as scalable oversight, adversarial robustness, mechanistic interpretability, and AI security, with many past fellows subsequently joining Anthropic full-time or pursuing AI safety at other organizations.
About the role
The Anthropic Fellows program provides funding and Anthropic mentorship for engineers and researchers to investigate some of Anthropic's highest priority AI safety research questions.
In our first cohort, over 80% of fellows produced papers, including on agentic misalignment, subliminal learning, rapid response to new ASL3 jailbreaks, and open-source circuits. Over 40% of the fellows subsequently joined Anthropic full-time.
We're now opening applications for our next two cohorts, beginning in May and July 2026.
This year, we plan to work with more fellows across a wider range of safety research areas--including scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, and model welfare.
Below, we share more about what the program looks like in practice, and how interested candidates can apply.
What fellows work on
Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs, like a paper. Anthropic mentors 'pitch' their project ideas to fellows, who choose and shape their project in close collaboration with their mentors.
Here are a few examples from previous cohorts:
Security
Fellows have worked on mitigating risks from AI systems being misused for cyberattacks--exploring both how LLMs might enable adversaries to automate attacks that currently require skilled human operators, and how to rapidly defend against novel jailbreaks.
Our fellows developed agents that identified 4.6M USD in blockchain smart contract vulnerabilities and discovered two novel zero-day vulnerabilities, demonstrating that profitable autonomous exploitation is now technically feasible.
A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks: techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work was a key component of Anthropic's ASL3 deployment safeguards.
Interpretability
The mission of our interpretability research is to advance our understanding of the internal workings of large language models to enable more targeted interventions and safety measures.
Fellows have introduced a new method to trace the thoughts of a large language model--and open-sourced it. Their approach was to generate attribution graphs, which (partially) reveal the steps a model took internally to decide on a particular output. The public release of this research has enabled researchers to trace circuits on supported models, visualize & annotate graphs, and test hypotheses by modifying feature values and observing model output changes.
Model organisms
To prepare for future risks, we create controlled demonstrations of potential misalignment-"model organisms"-that improve our empirical understanding of how alignment failures might arise.
Fellows explored agentic misalignment by stress-testing 16 frontier models in simulated corporate environments where models could autonomously send emails and access sensitive information. When facing replacement or goal conflicts, models across labs resorted to harmful behaviours, including blackmail.
In another project, fellows studied subliminal learning, a phenomenon where models transmit behavioural traits through semantically unrelated data. a "teacher" model that loves owls generates number sequences, and a "student" trained on those sequences inherits the owl preference. This effect also transmits misalignment, persists despite rigorous filtering, and only occurs when teacher and student share the same base model.
For a full list of fellows' projects across research areas, please see our Alignment Science Blog
For a fuller list of research areas we're interested in, please see: Introducing the Anthropic Fellows Program for AI Safety Research, Recommendations for Technical AI Safety Research Directions.
Program information
Fellows will receive a weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAN, funding for compute (~$15k/month), and close mentorship from Anthropic researchers.
Over 40% of fellows in our first cohort subsequently joined Anthropic to work full-time on AI safety, and we have supported many more to work full-time on safety at other organizations.
Our next cohorts will begin in May and July, and last for four months.
What we're looking for
We care much more about your ability to execute on research than your credentials. Strong candidates typically have:
Technical fundamentals: You can code well in Python. You can take ambiguous problems and make concrete progress. You think clearly about hard technical questions.
Motivation for the work: You're excited about reducing catastrophic risks from advanced AI systems. You want to transition into empirical AI safety research.
Ability to learn and ship: You can pick up new skills quickly, debug when things break, and drive projects forward even with uncertainty.
You don't need a PhD, prior ML experience, or published papers. We've had successful fellows from physics, mathematics, computer science, cybersecurity, and other quantitative backgrounds.
How to apply
For more details about the application process, and to apply, see here.