Anthropic Research Scientist in Life Sciences focusing on Computational Chemistry at Anthropic. Responsible for designing reinforcement learning environments and datasets to advance AI-driven chemistry research.
Responsibilities
Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. We're seeking a research scientist who combines deep chemistry knowledge with machine learning expertise to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be a computational chemist who knows reactions, synthesis, and medicinal chemistry in depth and has hands-on experience training and evaluating machine learning models. Above all, you'll build the reinforcement learning environments, datasets, and evaluations that teach Claude chemistry. Working alongside our bench chemists and AI researchers, you'll turn real chemistry problems and experimental data into training tasks and measure whether the models get better at them.
This role is fully on-site in the San Francisco Bay Area.
Design and build reinforcement learning environments for chemistry end to end, including task design, data curation, automated grading, and checks against shortcuts and data leakage
Design and source new experimental datasets, environments, and evaluations with our bench chemists, and external research and academic partners
Develop evaluations that measure model performance on real chemistry tasks, such as reaction outcome prediction, synthesis planning, structure elucidation, and compound design, and feed results back to research teams
Run and analyze training experiments with AI research teams, diagnose where models fail at chemistry, and prioritize what to build next
Ensure datasets and environments are high quality and well documented, including sources, train and test splits, and failure modes
Communicate findings clearly to chemistry, AI research, and life sciences audiences
Qualification
PhD in chemistryExperience with reinforcement learningPublicationsFor sales roles
Required
PhD in chemistry, computational chemistry, or a related field
Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, and SAR, with experimental data as your 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 while collaborating effectively across cross-functional teams, with strong written and verbal communication skills
Preferred
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 the literature, patents, or lab notebooks, including reaction, ADMET, and spectroscopic data
Cheminformatics and molecular modeling skills, for example 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
The annual compensation range for this role is listed below.
For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
