Research Scientist, Life Sciences (Computational)

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The Life Sciences team combines cutting-edge AI with hands-on biological research to make fundamental biological discoveries. This role is for a computational research scientist who will work at the intersection of computational and experimental biology, using large-scale computational analysis, infrastructure engineering, and LLM-based tools (including Claude) to drive experiments and discoveries.

Responsibilities

• Build, run, and maintain analysis pipelines that back the team's experimental programs, including sequence analysis at petabyte scale, structural bioinformatics, phylogenetic and comparative genomics, design and analysis of high-throughput functional screens, and biological sequence modeling.
• Partner directly with experimental biologists to design experiments that produce high-quality data and rapidly iterate analyses to 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 accessible to researchers and AI agents.
• Use Claude and internal agent frameworks heavily in your work and provide feedback (evaluations, datasets, failure cases) to model-improvement and product teams.
• Pick up analyses across projects as priorities shift; prioritize breadth and flexibility across computational biology domains.

Minimum qualifications

• PhD in computational biology, bioinformatics, genomics, biophysics, machine learning, computer science, or a related quantitative or biological field, or equivalent industry research experience.
• A track record of computational biology research led end-to-end (question to result) with evidence of impact (publications, preprints, released datasets/tools, or research that changed a program's direction).
• Demonstrated breadth across multiple areas of computational biology.
• Proficiency in one or more programming languages used in scientific computing and comfort working on large datasets in Linux and cloud compute environments.
• Ability to scope ambiguous biological questions and produce results that experimentalists can act on.
• Ability to communicate computational results clearly to both biologists and ML researchers.

Preferred qualifications

• Comfortable navigating ambiguity and developing solutions in rapidly evolving research environments; results-oriented with a bias toward flexibility and impact.
• Hands-on experience in experimental biology or a track record of designing experiments alongside 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) in addition to required breadth.

Compensation

• Annual salary: $300,000 - $320,000 USD

Logistics

• Location: San Francisco, CA (Anthropic headquarters)
• Location-based hybrid policy: staff are expected to be in one of the offices at least 25% of the time.
• Minimum education: Bachelor’s degree or equivalent combination of education/training/experience (minimum qualifications list a PhD or equivalent industry experience).
• Minimum years of experience: Will correlate with internal job level requirements for the position.
• Visa sponsorship: Anthropic states they sponsor visas and retain an immigration lawyer to assist, though sponsorship is not guaranteed for every role/candidate.

How we're different / Other notes

• The team emphasizes large-scale, collaborative AI research and values communication skills. Candidates are encouraged to apply even if they do not meet every qualification. The posting includes guidance on candidate AI usage and safety notes for avoiding scams from non-anthropic domains.