Apply using this link. Applications for the next cohort of Anthropic Fellows close at 11:59pm PT on July 26. The cohort is expected to start November 2. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the November start date doesn't work for you.
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.
- 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
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. 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.
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).
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:
- ML Systems & Performance Fellows
- Reinforcement Learning Fellows
- The Anthropic Institute Fellows (Economics & Policy)
- 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 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
- Fluent in Python programming
- Available to work full-time on the Fellows program
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.
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
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
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
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
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
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
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
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
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 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 recruiting partner. Clicking "Apply here" will take you to their portal, and updates will come from a Constellation address. Constellation also runs the Berkeley workspace and provides program support for fellows working on AI safety and security; fellows on capabilities-focused projects are supported directly by Anthropic. All applicants currently use the same application portal but we are working to separate applications for safety/security and capabilities focused projects in future rounds.
The below are Anthropic's policies for full time roles. These do NOT apply to the Fellows Program.
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.
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.
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.
