Anthropic’s Life Sciences team seeks a Research Scientist to drive AI‑accelerated discoveries in biology. The role blends deep computational biology with frontier AI, building large‑scale analysis pipelines, collaborating with experimental biologists, and integrating Claude and internal agent frameworks into research workflows. The position is onsite in San Francisco and offers competitive compensation, optional equity donation matching, generous vacation and parental leave, flexible hours, and a collaborative office environment.
Research Scientist, Life Sciences (Computational) at Anthropic
On-site - San Francisco, CA
More jobs at AnthropicRequirements
Skills
- 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
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
Technologies
scientific computing programming languagesLinuxcloud compute environmentsClaudeinternal agent frameworks
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