Anthropic AI Security Fellow

London, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CAFull-TimeMid-levelAI / Data Science

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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.

AI Security at Anthropic

  • We believe we are at an inflection point for AI’s impact on cybersecurity. Models are now useful for cybersecurity tasks in practice: for example, Claude can now outperform human teams in some cybersecurity competitions and help us discover vulnerabilities in our own code.
  • We are looking for researchers and engineers to help us accelerate defensive use of AI to secure code and infrastructure.

Anthropic Fellows Program Overview

  • The Anthropic Fellows Program is designed to accelerate AI security and safety research, and foster research talent. We provide funding and mentorship to promising technical talent - regardless of previous experience - to research the frontier of AI security and safety for four months.

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 our previous cohorts, over 80% of fellows produced papers (more below).

  • We run multiple cohorts of Fellows each year. This application is for cohorts starting in July 2026 and beyond.

What to Expect

  • Direct mentorship from Anthropic researchers
  • Access to a shared workspace (in either Berkeley, California or London, UK)
  • Connection to the broader AI safety research community
  • Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD & access to benefits (benefits vary by country)
  • Funding for compute (~$15k/month) and other research expenses

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

You may be a good fit if you

  • Are motivated by reducing catastrophic risks from advanced AI systems
  • Are excited to transition into full-time empirical AI safety research and would be interested in a full-time role at Anthropic

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 here at Anthropic. In previous cohorts, over 40% of fellows received a full-time offer, and we’ve supported many more to go on to do great work on safety at other organizations.

  • Have a strong technical background in computer science, mathematics, physics, cybersecurity, or related fields
  • Thrive in fast-paced, collaborative environments
  • Can implement ideas quickly and communicate clearly

Strong candidates may also have

  • Contributed to open-source projects in LLM- or security-adjacent repositories
  • Demonstrated success in bringing clarity and ownership to ambiguous technical problems
  • Experience with pentesting, vulnerability research, or other offensive security
  • A history demonstrating desire to do the "dirty work" that results in high-quality outputs
  • Reported CVEs, or been awarded for bug bounty vulnerabilities
  • Experience with empirical ML research projects
  • Experience with deep learning frameworks and experiment management

Candidates must be

  • Fluent in Python programming
  • Available to work full-time on the Fellows program for 4 months

Interview process

  • The interview process will include an initial application & references 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).

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.

  • 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.

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.

Job Summary

CompanyAnthropic
LocationLondon, UK; Ontario, CAN; Remote-Friendly, United States; San Francisco, CA
TypeFull-Time
LevelMid-level
DomainAI / Data Science