Research Scientist, Life Sciences (Computational Chemistry)
$300k - $320k • San Francisco, CA
Posted 14h ago
Job Location
San Francisco, CA
Tech Stack
Remote Work Policy
On-site
Categories
AI Research Engineer
About the job
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.
Responsibilities
- Design and build reinforcement learning environments for chemistry, including task design, data curation, automated grading, and checks against shortcuts and data leakage.
- Design and source new experimental datasets, environments, and evaluations with bench chemists and external research partners.
- Develop evaluations to measure model performance on real chemistry tasks like reaction outcome prediction, synthesis planning, structure elucidation, and compound design.
- Run and analyze training experiments, diagnose model failures in chemistry, and prioritize future development.
- Ensure datasets and environments are high quality and well documented, including sources, train/test splits, and failure modes.
- Communicate findings clearly to chemistry, AI research, and life sciences audiences.
Requirements
- 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 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 communication skills.