Research Scientist, Life Sciences; Experimental Biology

Research Scientist, Life Sciences (Experimental Biology)

San Francisco, CA

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

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.

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

• 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 (Uni Prot, Gen Bank, PDB) and computational biology tools

This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for human health and scientific understanding. If you're excited about using your expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.

The annual compensation range for this role is listed below. For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$300,000 - $320,000 USD

Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all…