Description
Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery.
We're seeking an exceptional Research Scientist to join the team. As a founding member of Life Sciences, you'll work in a high-impact group that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science.
Key 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 computational predictions.
- Use Claude and internal agent frameworks heavily in your own work , for experimental planning, protocol development, and data interpretation , and feed what you learn back to model-improvement and product teams as evaluations, datasets, and concrete failure cases.
Minimum qualifications:
- Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology) or a related field.
- Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems.
- Basic proficiency in Python and familiarity with ML development practices.
Preferred qualifications:
- 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.
The annual compensation range for this role is $300,000-$320,000 USD.