Anthropic
Research Scientist, Life Sciences (Experimental Biology)
$300K–$320K
San Francisco, CA +1 · Full-time · Posted Jul 8, 2026
- Location
- San Francisco, CA
San Francisco, California, United States - Experience
- Years required correlate with the internal job level; no specific number stated.
- Education
- Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Required field of study: a relevant field demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is available, but not for every role or candidate; the company says it will make reasonable efforts to obtain a visa for an offer recipient and provides immigration counsel.
About the job
Designs and runs experimental biology research that combines laboratory work and computational predictions to make biological discoveries and inform AI model and product development.
What you'll do
- Design, execute, and iterate on experiments in molecular biology, biochemistry, protein and nucleic acid characterization, functional screens, and assay development.
- Collaborate with computational biologists to generate analysis-ready data and use results to guide further analysis.
- Use Claude and internal agent frameworks for research tasks, and provide evaluations, datasets, and failure cases to model-improvement and product teams.
What they ask for
- Ph.D. in a biological science or related field.
- Track record of combining biological expertise with computational approaches to solve scientific problems.
- Basic proficiency in Python and familiarity with ML development practices.
Nice to have
- Published research or practical experience in scientific AI applications.
- Familiarity with modern machine learning techniques, model training methodologies, biological databases, or computational biology tools.
Pay
$300,000—$320,000 USD (annual salary)
Tools
Python, Claude, UniProt, GenBank, PDB
AI focus
Uses AI tools and internal agent frameworks for experimental planning, protocol development, and data interpretation; shares evaluations, datasets, and failure cases to improve models and products.
Model-written summary · the original posting is authoritative