Anthropic Fellows Program, AI Safety & Security
Anthropic London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CAEst. Est. USD 90,000–130,000 / yearMid
Estimated range based on role, country and industry — not published by the company.
Key requirements
- Python
- Deep 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.
Applications are now open for our January cohort - apply here.
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.
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.
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).
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
Collin Burns
Fabien Roger
Samuel Marks
Kyle Fish
Ethan Perez
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 adversaria
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