Research Scientist, Life Sciences (Experimental Biology)

Anthropic•San Francisco, CA

8d•$300,000 - $320,000•Hybrid

About The Position

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.

Requirements

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
Have basic proficiency in Python and are familiar with ML development practices

Nice To Haves

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

Responsibilities

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