Anthropic’s Life Sciences team is building a world‑class research group focused on making fundamental biological discoveries, combining cutting‑edge AI with hands‑on biological research. As a founding member, you will work at the intersection of computational and experimental biology, helping establish Anthropic as a leader in biology research while developing product intuition through direct engagement with laboratory science.
Research Scientist, Life Sciences (Experimental Biology) at Anthropic
Hybrid - San Francisco, CA, USA
More jobs at AnthropicSalary
USD 300,000 - 320,000
Requirements
Skills
- Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology) or related field
- Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
- Basic proficiency in Python
- Familiarity with machine learning development practices
- Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Ability to work independently while maintaining strong collaboration with cross-functional teams
- Results-oriented with a bias towards flexibility and impact
- Published research or practical experience in scientific AI applications
- Familiarity with modern machine learning techniques and model training methodologies
- Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
Responsibilities
- Design, execute, and iterate on experimental programs at the core of the team's research: molecular biology, biochemistry, protein and nucleic acid characterization, high-throughput functional screens, and assay development
- Partner directly with computational biologists to design experiments that produce high-quality, analysis-ready data, and feed results back fast enough to immediately inform the next round of analysis
- Generate and prioritize hypotheses by combining experimental judgment with literature, curated biological knowledge bases, and the team's computational predictions
- Use Claude and internal agent frameworks heavily in experimental planning, protocol development, and data interpretation, and feed learnings back to model-improvement and product teams
Technologies
PythonMachine Learning development practicesClaudeInternal agent frameworksBiological databases (UniProt, GenBank, PDB)Computational biology toolsModern machine learning techniquesModel training methodologies
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