Research Scientist, Life Sciences

Details

Anthropic is seeking a Research Scientist to join its Life Sciences team, which focuses on making Claude a superhuman life sciences research assistant. The role sits at the intersection of machine learning, software engineering, and biology, with a focus on improving model capabilities through post-training, evaluation design, and reinforcement learning environment development.

The position involves translating biological domain knowledge into model training objectives, benchmarks, and agentic workflows, while helping advance AI-accelerated biology and computational biology capabilities.

Responsibilities

• Build and ship agentic tools and integrations that enable Claude to execute life science workflows, including bioinformatics pipelines, database queries, analysis notebooks, and literature reviews.
• Design and build evaluation benchmarks for biology tasks such as figure interpretation, bioinformatics, protocol reasoning, and literature synthesis.
• Work 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 engineering infrastructure for the biology product surface, including tool scaffolding, data pipelines, and evaluation harnesses.
• Translate biological domain knowledge into product requirements and evaluation criteria that guide model improvement.

Requirements

• Experience applying machine learning and software engineering to biological problems, such as computational biology, bioinformatics, protein machine learning, or genomics.
• Experience working in drug discovery or development at a biotech or pharmaceutical company, or conducting fundamental research in an academic setting.
• Strong software engineering skills, including production-quality Python development, working in large codebases, and owning infrastructure end to end.
• Hands-on experience training or fine-tuning machine learning models, including large language models, protein language models, or other deep learning architectures.
• A track record of shipping computational tools or pipelines used by biologists.
• Ability to navigate ambiguity and define problems in a rapidly evolving research environment.
• Ability to work independently while collaborating with research, product, and domain-expert teams.
• Results-oriented approach with a focus on rapid iteration and measurable impact.
• Passion for using AI to accelerate scientific discovery while maintaining high ethical standards.

Preferred Qualifications

• Five or more years of experience applying machine learning and software engineering to biological problems.
• Ph.D. in computational biology, bioinformatics, bioengineering, computer science, or a related quantitative field, or equivalent industry experience.
• Experience with large language model post-training, including RLHF, reinforcement learning from verifiable rewards, supervised fine-tuning data curation, or evaluation-driven development.
• Direct experience with therapeutic discovery pipelines, including target identification, lead optimization, ADMET modeling, or clinical data analysis.
• Familiarity with bioinformatics tooling and pipelines, including sequence analysis, structure prediction, single-cell analysis, and variant calling.
• Experience building agentic systems or tool-use environments.
• Published research in machine learning for biology or open-source contributions to computational biology tools.
• Fluency with biological databases such as UniProt, PDB, Ensembl, and NCBI, including the ability to reason about their schemas and failure modes.

Education

• Minimum education: Bachelor's degree or an equivalent combination of education, training, and experience.
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience.

Benefits

Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and an office space for collaboration.