Director/Senior Director, AI/ML for Drug Discovery
Septerna · South San Francisco, California, United States
About this role
**Septerna — Director/Senior Director, AI/ML for Drug Discovery** Septerna is a biotechnology company based in South San Francisco, CA, focused on developing life-changing medicines. Using its **Native Complex Platform™**, Septerna recapitulates GPCRs with their native structure, function, and dynamics outside of the cellular context—enabling novel access to modern structural and screening technologies for the entire class of GPCRs. This role is an opportunity to lead and expand the **Data Science** function, applying advanced **AI/ML** and statistical methods to leverage Septerna’s proprietary **GPCR structural and assay data** to advance **small-molecule drug discovery** programs. --- ### **The Role** Lead a **visionary, hands-on** Data Science organization. You will create and execute a strategy to fully leverage Septerna’s proprietary data, collaborating closely with **Computational Chemistry**, **Structural Biology**, and other cross-functional research teams to accelerate hypothesis generation and scientific discovery. --- ### **Key Responsibilities** - Lead Septerna’s **Data Science team**: provide technical direction, develop team capabilities, and integrate/prioritize projects (with a primary focus on machine learning). - Develop and execute a roadmap for **state-of-the-art AI/ML** approaches using Septerna’s GPCR structural data to discover and refine novel chemical matter. - Identify and integrate productive emerging technologies into Septerna’s computational stack (via internal development, partnerships, or both). - Oversee and extend analysis pipelines, including **DEL**, **HTS**, and assay-data workflows; identify opportunities to advance Data Science within the broader Platform development team. - Advance and support **ML-QSAR** models using internal and external assay and ADMET data to improve the efficiency of **DMTA** cycles and support **generative chemistry** workflows. - Refine data-engineering and cloud-infrastructure requirements; establish best practices for **data curation** and **model validation**. --- ### **Required Qualifications** - **PhD** in computational biology, computational chemistry, or a degree in computer science, applied mathematics, or statistics focused on modeling biological systems. - **10+ years** of relevant industry experience, including applying machine learning to diverse scientific problems. - Demonstrated leadership in ML for drug discovery, with a track record building and applying predictive models to real-world, noisy, multimodal scientific datasets. - Experience applying **co-folding or hybrid models** for ligand pose prediction (ideally for membrane proteins and flexible protein classes). - Experience with **transfer learning / fine-tuning foundation models**, including using appropriate metrics to show performance improvements and characterize applicability domain. - Experience leading teams and serving as senior technical lead for cross-functional initiatives, applying technical insight and scientific judgment to guide research strategy. --- ### **Technical & Platform Requirements** - Expertise in scientific machine learning and drug discovery dependencies: **PyTorch, scikit-learn, NumPy, pandas, pyarrow, RDKit**. - Deep knowledge of modern ML/statistical methods, including: **XGBoost/LGBM**, **GNNs (GNN/D-MPNN)**, **transformers**, **generative modeling**, **active learning**, and **Bayesian optimization**. - Extensive experience with **cloud/distributed computing** (A
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