Anthropic’s Life Sciences team is building a world‑class research group focused on AI‑accelerated biological discovery. As a Research Scientist, you will develop and maintain large‑scale computational pipelines, collaborate with experimental biologists, generate hypotheses from data, and contribute to the team’s AI infrastructure. You’ll leverage Anthropic’s Claude model and internal agent frameworks to drive scientific insights and help shape the future of AI in biology.
Research Scientist, Life Sciences (Computational) na Anthropic
Presencial - San Francisco, CA
Ver mais vagas na AnthropicRequirements
Skills
- PhD in computational biology, bioinformatics, genomics, biophysics, machine learning, computer science, or a related quantitative or biological field, or equivalent industry research experience
- Track record of computational biology research led end-to-end, with evidence of impact such as publications, preprints, released datasets or tools, or research that changed a program's direction
- Demonstrated breadth across multiple areas of computational biology
- Proficient in one or more programming languages used in scientific computing and comfortable working on large datasets in Linux and cloud compute environments
- Ability to take an ambiguous biological question, scope the analysis, and produce a result that an experimentalist can act on
- Clear communication of computational results to both biologists and ML researchers
- Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
- 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
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 interfaces that make all of it accessible to researchers and AI agents
- Use Claude and 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; prioritize breadth and flexibility over a single deep specialty
Technologies
ClaudeLLMsLinuxcloud compute environments
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