- Lead cheminformatics on multiple drug discovery programs, analyzing data with medicinal chemists and biologists each week and shaping which compounds get made.
- Expand Octant’s cheminformatics and ML capabilities, including methods for iterative exploration of chemical space, development of predictive ADME models and scoring functions, and help set our long-term computational strategy as the field moves.
- Collaborate with chemists to design and test chemical libraries on our nanoscale and microscale synthesis platform, guiding hit-to-lead and lead optimization.
- Develop and contribute to the data and computational infrastructure, enabling scientists to access and analyze their own data in consistent, statistically rigorous ways.
- Increase scientist leverage by building the agentic layer of our discovery workflows: encode cheminformatics methods as tools that AI agents use to propose, triage, and analyze compounds alongside program scientists with the appropriate strategies and safeguards for decision making.
This job might be great for you if you have
- An advanced degree (preferably a PhD) in chemistry, computer science, statistics, or a related field.
- 2+ years of drug discovery experience, academic or industry.
- Working knowledge of medicinal chemistry: you can reason about structure-property relationships and be fluent chemically in discussions with chemists.
- Deep cheminformatics expertise (SAR analysis, library enumeration, property calculation) and fluency with the standard toolkits (e.g. RDKit, Datamol, OEChem).
- Applied machine learning to chemical problems, including QSAR or property models that were deployed and used on live programs; comfort with the relevant stack (e.g. PyTorch, JAX, scikit-learn, DeepChem).
- Experience working side-by-side with medicinal and computational chemists, data scientists, and engineers, including influencing which molecules get made; an instinct for building analyses and tools that scientists can run themselves.
- Strong software skills: fluent in Python and its data stack (NumPy, pandas, Polars), experienced with cloud infrastructure, databases, and version control; writes reproducible analyses that other people can run and build on.
- Familiarity with structure-based computational chemistry methods (protein structure analysis, docking, MD) and interest in going deeper.
- Experience curating public chemical datasets (e.g. ChEMBL, PubChem) and using them to augment internal models.
- Built tooling and infrastructure for agentic workflows.
Optional: Along with your application, please share a paper/preprint/software repo that best highlights your strengths so we can better understand work you've led. If there is nothing public, please summarize that work in a few paragraphs. We'd love to also hear about things you've been proud to build or lead outside of science.
The expected annual salary for this role is in the range of $145,000 to $185,000, depending on experience level. We offer health benefits (medical, dental, vision), equity, and 401(k) matching.
Octant is located in Emeryville, California. We primarily work onsite, but are open to remote hires for this role.
Octant’s navigator platform
Octant’s Platform, “The Navigator,” accelerates the design, build, test, & learn iterations across chemistry and biology to build drugs against cellular mechanisms. The Navigator is an expanding set of drug discovery and development capabilities with three interwoven components that operate together:
- Generative Biology – Multiplexed measurements from engineered sensors in human cells to understand cellular mechanisms
- Generative Chemistry – Nanoliter-scale chemical synthesis for rapid exploration of local & targeted chemical space
- Generative AI – ML/AI ingests large-scale biological and chemical datasets and drives iteration of experiments to build better molecules faster
Together, the Navigator platform drives week-to-week progress on our drug programs, generating 3M+ readouts each week across multiple therapeutic areas.