Anthropic
Research Counsel
$265K–$335K
San Francisco, CA +2 · Full-time · Posted Sep 3, 2026
- Location
- San Francisco, CA
New York City, NY
San Francisco, California, United States - Experience
- At least 8 years preferred; required years correlate with internal job level requirements.
- Education
- Law degree (JD, LLB, or equivalent). Minimum education: bachelor’s degree or an equivalent combination of education, training, and/or experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; if an offer is made, Anthropic will make every reasonable effort to obtain a visa and provides an immigration lawyer.
About the job
Advises research and data teams on legal issues throughout frontier AI development, including data use, research, model launches, regulation, and safety.
What you'll do
- Advise on data sourcing, licensing, partnerships, and use, and help shape data strategy.
- Coordinate regulatory readiness and model launches, review documentation, and respond to regulatory inquiries.
- Counsel research teams on design, collaborations, publications, and biological and chemical risk policy.
What they ask for
- Law degree (JD, LLB, or equivalent) and meet local licensing requirements to work as in-house counsel.
- Familiarity advising on the EU AI Act, U.S. legislation and AI frameworks, GDPR/CCPA, and comparable global regimes.
- Strong communication, analytical, and technical fluency for advising on complex, ambiguous legal and technical questions.
Nice to have
- At least 8 years of legal experience advising technology companies on copyright, privacy, data, AI regulation, and related product and policy issues; ideally including in-house and AI/ML work.
- Experience with regulators or standards bodies, technical research, academic publishing, licensing, biosecurity, or safety frameworks.
Pay
$265,000 — $335,000 USD annual salary (base salary)
Tools
EU AI Act, GDPR/CCPA, AI/ML, Responsible Scaling Policy
AI focus
Provides legal advice across frontier AI research and development, including training data, model evaluations and launches, alignment, interpretability, societal impacts, and biological and chemical risk.
Model-written summary · the original posting is authoritative