What you’ll bring
- A PhD in chemistry, computational chemistry, cheminformatics, or a related field—or equivalent research experience.
- Deep knowledge of organic and medicinal chemistry, including reactions, synthesis, structure–activity relationships, and the practical tradeoffs involved in drug discovery.
- Strong Python skills and a solid foundation in machine learning, with hands-on experience training and evaluating models.
- Scientific judgment: you can distinguish a promising benchmark result from a meaningful advance in a discovery workflow.
- Comfort working through ambiguity, choosing priorities, and taking a project from an open question to a concrete result.
- A collaborative approach, clear communication, and the willingness to work across research and engineering as the team grows.
Experience that would stand out
- Reinforcement learning, large language models, scientific agents, or chemistry-focused evaluations and benchmarks.
- Contributions to drug discovery programs in close collaboration with medicinal or synthetic chemists.
- Experience curating reaction, bioactivity, ADMET, or spectroscopic datasets, with attention to provenance, quality, and data leakage.
- Familiarity with tools and methods such as RDKit, molecular docking, molecular dynamics, free energy calculations, or quantum chemistry.
- Hands-on synthetic or medicinal chemistry experience at the bench.
- Publications, patents, open-source tools, or deployed research systems that demonstrate scientific impact.
You don’t need to check every box. We’re interested in people who bring depth, curiosity, and the drive to build useful things—and who want to help shape what comes next.