[Hiring] Scientist, Discovery Cheminformatics @Octant Bio

Role Description

Octant is seeking a scientist with a strong background across the chemical and computational domains. You will apply established and emerging cheminformatics and machine learning methods to drive Octant’s small molecule drug discovery programs. This is a unique opportunity to leverage and shape our iterative, direct-to-biology chemical synthesis platform and expansive datasets, all in the context of live programs.

In this interdisciplinary role, you will engage the chemistry and biology of our programs through a computational lens. You are motivated by the mission of Octant and have a team-first mindset. You bring excellent communication skills and an ability to thrive in a dynamic and fast-paced environment.

In this role, you will:

• 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• 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.

Company Description

Octant is a clinical-stage drug discovery company developing correctors for protein misfolding diseases with active programs in rare diseases and oncology. Octant’s platform, The Navigator, combines high-throughput synthetic biology, generative chemistry, and AI/ML to discover and develop therapeutics against complex cellular mechanisms in human cells.