Anthropic is seeking an exceptional Research Scientist for its Life Sciences team in San Francisco. The role sits at the intersection of machine learning, software engineering, and biology, focusing on building a world‑class research group that turns computational biology expertise into model capabilities. Responsibilities include building agentic tools, designing evaluation benchmarks, collaborating with product and design teams, partnering with external biotech and academic stakeholders, and maintaining engineering infrastructure. The position requires strong experience in ML applied to biological problems, drug discovery or academic research background, robust Python engineering skills, and a proven record of shipping computational tools. Anthropic offers competitive compensation, optional equity donation matching, generous leave policies, flexible hours, and a hybrid work arrangement with a presence in the San Francisco office.
Research Scientist, Life Sciences at Anthropic
Hybrid - San Francisco, CA
More jobs at AnthropicSalary
USD 300,000 - 320,000
Requirements
Skills
- Experience applying ML and software engineering to biological problems — computational biology, bioinformatics, protein ML, genomics, or similar
- Experience working in drug discovery or development at a biotech or pharma company, or conducted fundamental research in an academic setting — with an understanding of real scientific workflows and their limitations
- Strong software engineering skills: comfortable building production-quality Python, working in large codebases, and owning infrastructure end-to-end
- Hands‑on experience training or fine‑tuning ML models (LLMs, protein language models, or other deep learning architectures)
- A track record of shipping computational tools or pipelines that biologists actually use
- Comfortable navigating ambiguity and defining problems in a rapidly evolving research environment
- Able to work independently while collaborating tightly with research, product, and domain‑expert teams
- Results‑oriented with a bias toward rapid iteration and measurable impact
- Passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
Responsibilities
- Build and ship agentic tools and integrations that let Claude execute real life science workflows — bioinformatics pipelines, database queries, analysis notebooks, literature review
- Design and build evaluation benchmarks that measure model capabilities on biology tasks — figure interpretation, bioinformatics, protocol reasoning, literature synthesis
- Work closely with product and design teams to scope, prototype, and ship features for life sciences users
- Partner with external biotech, pharma, and academic users to understand their workflows and turn feedback into product improvements
- Build and maintain the engineering infrastructure behind our biology product surface — tool scaffolding, data pipelines, eval harnesses
- Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement
Technologies
PythonLarge language models (LLMs)Reinforcement learning from human feedback (RLHF)Reinforcement learning from verifiable rewardsSupervised fine‑tuning (SFT)Bioinformatics tooling and pipelines (sequence analysis, structure prediction, single‑cell, variant calling)Biological databases (UniProt, PDB, Ensembl, NCBI)ML for biology
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