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
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
What they're looking for
- 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
