Research Scientist, Life Sciences (Computational)

Anthropic•San Francisco, CA

19h•$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. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery. As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic. 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 scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.

Requirements

Have a PhD in computational biology, bioinformatics, genomics, biophysics, machine learning, computer science, or a related quantitative or biological field, or equivalent industry research experience
Have a track record of computational biology research you have led end to end, from question to result, with evidence of impact (for example publications, preprints, released datasets or tools, or research that changed a program's direction)
Have demonstrated breadth across multiple areas of computational biology
Are proficient in one or more programming languages used in scientific computing and comfortable working on large datasets in Linux and cloud compute environments
Can take an ambiguous biological question, scope the analysis, and produce a result an experimentalist can act on
Communicate computational results clearly to both biologists and ML researchers

Nice To Haves

Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
Are results-oriented, with a bias towards flexibility and impact
Hands-on experience in experimental biology, or a track record of designing experiments side by side with experimentalists
Experience building tools, pipelines, or agentic systems on top of LLMs, or training models on biological sequence data
Deep expertise in one or two areas of computational biology (for example structural biology, metagenomics, single-cell genomics, or protein design) on top of the required breadth

Responsibilities

Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, biological sequence modeling, etc.
Partner directly with experimental biologists to design experiments that produce high-quality data, and turn results around fast enough to immediately inform the next experiment
Draw on the literature and curated biological knowledge bases alongside primary data to generate and prioritize hypotheses for experimental follow-up
Stand up and maintain the team's computational infrastructure: data ingestion, workflow orchestration, internal databases, and the interfaces that make all of it accessible to both researchers and AI agents
Use Claude and our internal agent frameworks heavily in your own work, and feed what you learn back to the model-improvement and product teams as evaluations, datasets, and concrete failure cases
Pick up analyses across projects as priorities shift; we're looking for breadth and flexibility over a single deep specialty