Anthropic, the AI safety company behind the Claude AI models, has opened applications for the Anthropic Residency. It’s a paid, fixed-term research programme for established technical leaders who want to move into full-time work on making AI systems safe. Residents work on real research problems with Anthropic’s teams for six weeks, three months or six months, and are paid the equivalent of US$350,000 to US$500,000 a year.
You don’t need a background in machine learning, a PhD or published papers. Applications for this round close on 22 November 2026.
At a Glance
| Organisation | Anthropic |
| Programme | Anthropic Residency (paid, fixed-term research placements) |
| Who it’s for | Professors in quantitative fields, technical founders, staff-level engineers |
| Length | 6 weeks, 3 months or 6 months (flexible start dates) |
| Pay | US$350,000–500,000 a year equivalent |
| Location | San Francisco preferred; remote possible, especially for shorter placements |
| Deadline | 22 November 2026 |
What Residents Do
Residents work on empirical research problems set by Anthropic researchers and matched to their interests and background. Areas include:
- AI safety and alignment: scalable oversight, adversarial robustness and AI control, “model organisms” of misalignment, interpretability, safeguards research and AI welfare
- AI security and frontier red-teaming: stress-testing frontier models in areas such as cybersecurity, robotics or cryptography
- ML systems and reinforcement learning: improving how frontier models are trained, and the infrastructure behind that research
Benefits
- Pay that reflects seniority: US$350,000–500,000 a year equivalent
- A dedicated Anthropic mentor with regular one-to-one guidance. Potential mentors lead teams in alignment, interpretability, scalable oversight and AI security.
- Support to learn the field: dedicated self-study time, talks and guidance to get up to speed on modern ML and AI safety
- Access to frontier models and a dedicated compute budget
- Flexible timing and start dates
- A path to full-time AI research: Anthropic aims for every Resident to be ready to keep working on AI research full-time, at Anthropic or elsewhere
Eligibility
Anthropic is looking for people who:
- Have led ambitious work in a technical field, for example as a professor in a quantitative discipline, a technical founder or a staff-level engineer
- Are motivated by making AI safe and beneficial for society
- Want to move into empirical AI research full-time
- Thrive in fast-paced, collaborative environments
- Can implement ideas quickly and communicate clearly
Not required: a PhD, prior machine learning experience or published papers. Anthropic encourages applications from groups underrepresented in tech.
Note for applicants outside the US and UK: applications go through Anthropic’s US or UK job posting. Anthropic prefers Residents to work from San Francisco but says remote participation is possible, especially for shorter placements. Its announcement doesn’t cover visas or work authorisation, so check the details on the posting before you apply.
How to Apply
- Read Anthropic’s official announcement (link below).
- Apply through the US or UK Residency posting linked from that announcement, or find it on Anthropic’s careers page.
- Say which placement length you’d prefer: six weeks, three months or six months.
- Submit before 22 November 2026. Applicants may also be considered for the Anthropic Fellows Program.
Official announcement: Anthropic Residency (with US and UK application links) · Anthropic careers
Tips
- Lead with what you’ve led. Anthropic wants people who have framed and driven ambitious technical work, so show research agendas, products or systems you’ve owned.
- Connect your field to AI safety. Physicists, mathematicians, neuroscientists and engineers have all moved into AI research, so explain how your skills carry over.
- Read the research first. Look at Anthropic’s alignment, interpretability and red-teaming publications and mention the areas that interest you.
- Choose a realistic placement length. If you can’t relocate, a shorter remote placement may suit you better.
