Anthropic
[Expression of Interest] Research Manager, Interpretability
$350K–$500K
San Francisco, CA +1 · Manager · Full-time · Posted Nov 7, 2025
- Location
- San Francisco, CA
San Francisco, California, United States - Experience
- At least 2–5 years of management experience; the posting separately says required years of experience correlate with internal job-level requirements.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not guaranteed for every role or candidate; the company says it will make every reasonable effort to obtain a visa after an offer.
About the job
Manage and support an interpretability research team by planning and delivering projects, developing people, coordinating hiring and cross-team collaboration, and maintaining understanding of the team’s technical work and AI safety implications.
What you'll do
- Partner with a research lead on team direction, planning, execution, hiring, and people development.
- Improve team execution and processes; coach team members and support their career development.
- Lead recruiting and facilitate cross-team collaboration; communicate updates and results to leadership.
What they ask for
- At least 2–5 years of experience managing highly technical research and/or engineering teams.
- Background in machine learning, AI, or a related technical field.
- Experience with people management, including coaching, performance evaluation, career development, and technical hiring.
- Strong project management, communication, and cross-functional coordination skills; experience managing technical teams through ambiguity and change.
Nice to have
- Experience scaling engineering infrastructure or working on open-ended foundational research.
- Familiarity with Anthropic’s work and mechanistic interpretability.
Pay
$350,000 — $500,000 USD base salary
AI focus
Manage a team conducting mechanistic interpretability research to understand how language models work internally and support AI safety.
Model-written summary · the original posting is authoritative