Research Scientist, Life Sciences (Computational Chemistry)

Anthropic•San Francisco, CA

4d•$300,000 - $320,000•Onsite

About The Position

Anthropic’s Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. The Research Scientist will bring deep chemistry knowledge and machine learning expertise to help accelerate progress in the life sciences. The role focuses on building reinforcement learning environments, datasets, and evaluations that teach Claude chemistry, translating real chemistry problems and experimental data into training tasks, and measuring model progress. The scientist will work with bench chemists and AI researchers and is fully on-site in the San Francisco Bay Area.

Requirements

PhD in chemistry, computational chemistry, or a related field.
Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, and structure–activity relationships (SAR), using experimental data as ground truth.
Strong Python skills and solid machine learning fundamentals, with hands-on experience training and evaluating models.
Comfort navigating ambiguity and defining problems in a rapidly evolving research environment.
Ability to work independently and collaborate effectively across cross-functional teams, with strong written and verbal communication skills.

Nice To Haves

Experience with reinforcement learning, large language models, or building evaluations and benchmarks.
Track record of supporting drug discovery programs alongside medicinal and synthetic chemists.
Experience curating chemistry datasets from literature, patents, or lab notebooks, including reaction, ADMET, and spectroscopic data.
Cheminformatics and molecular modeling skills, such as RDKit, docking, free energy calculations, or quantum chemistry.
Hands-on synthetic or medicinal chemistry experience at the bench.
Publications, patents, or open-source software demonstrating scientific impact.

Responsibilities

Design and build end-to-end reinforcement learning environments for chemistry, including task design, data curation, automated grading, and safeguards against shortcuts and data leakage.
Design and source experimental datasets, environments, and evaluations with bench chemists and external research and academic partners.
Develop evaluations of model performance on real chemistry tasks, including reaction outcome prediction, synthesis planning, structure elucidation, and compound design, and share results with research teams.
Run and analyze training experiments with AI research teams, diagnose chemistry-related model failures, and prioritize future work.
Ensure datasets and environments are high quality and well documented, including their sources, train/test splits, and failure modes.
Communicate findings clearly to chemistry, AI research, and life sciences audiences.