The opportunity
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.
What you'll do
Design, execute, and iterate on the experimental programs at the core of the: team's research: molecular biology, biochemistry, protein and nucleic acid characterization, high-throughput functional screens, and the assay development that makes new questions answerable
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 your experimental judgment: with the literature, curated biological knowledge bases, and the team's computational predictions
Use Claude and our internal agent frameworks heavily in your own work: for experimental planning, protocol development, and data interpretation — and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases
Have a Ph.D. in a biological science (molecular biology, biochemistry,: bioengineering, computational biology) or a related field
Have a track record of bridging biological domain knowledge with: computational approaches to solve real scientific problems
What they're looking for
- Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- Can work independently while maintaining strong collaboration with cross-functional teams
- Are results-oriented, with a bias towards flexibility and impact
- Thrive in a fast-paced research environment where you balance rigorous: scientific standards with rapid iteration
- 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
