Director, AI/ML for Drug Discovery

Septerna•South San Francisco, CA

19h•$240,000 - $270,000

About The Position

Septerna is seeking an experienced scientific leader with AI/ML expertise to lead its Data Science function. The role applies advanced machine-learning and statistical approaches to the company’s proprietary GPCR structural and assay data to advance small-molecule drug discovery programs. The leader will create and execute a strategy for fully leveraging Septerna’s data, working closely with Computational Chemistry, Structural Biology, and other cross-functional research teams to accelerate hypothesis generation and scientific discovery. This is a visionary, hands-on leadership role requiring knowledge of internal data assets, modern drug discovery, and advanced computational methods, along with the ability to build a high-impact function in a fast-moving environment. The role leads the Data Science team, develops its capabilities and technical direction, and establishes approaches for applying AI/ML, maintaining analysis pipelines, improving data engineering and cloud infrastructure, and supporting drug discovery workflows. The position is based in the context of Septerna’s GPCR-focused drug discovery programs. The anticipated salary range for candidates working in South San Francisco, California, is $240,000–$270,000. Full-time compensation also includes benefits, stock options, and an annual target bonus.

Requirements

PhD in computational biology or 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 machine learning for drug discovery, with a track record of building and applying predictive models to real-world, noisy, and multimodal scientific datasets.
Experience applying co-folding or hybrid models to enable productive ligand pose prediction, ideally on membrane proteins and flexible protein classes.
Experience transferring learning or fine-tuning foundation models and using appropriate metrics to demonstrate performance improvements and characterize applicability domain.
Experience leading teams and serving as the senior technical lead of cross-functional initiatives, with a track record of applying technical insight and scientific judgment to guide research strategy.
Expertise in scientific machine-learning and drug-discovery dependencies, including PyTorch, scikit-learn, NumPy, pandas, pyarrow, and RDKit.
Deep knowledge of modern machine-learning and statistical methods, including gradient boosting decision trees (XGBoost, LGBM), graph neural networks (GNN/D-MPNN), transformers, generative modeling, active learning, and Bayesian optimization.
Extensive experience with cloud and distributed computing environments, including AWS, GCP, or Azure, and building and maintaining containerized machine-learning workflows.
Strong track record in MLOps and partnering with software and platform engineering teams to define fit-for-purpose infrastructure for scientific applications.

Nice To Haves

Experience with molecular property prediction, generative chemistry, graph neural networks, protein–ligand modeling, sequence modeling, or physics-informed machine learning.
Hands-on structure-based drug discovery experience and experience collaborating closely with Computational Chemistry teams.
Publications, patents, or open-source contributions in relevant scientific or machine-learning domains.

Responsibilities

Lead Septerna’s Data Science team, providing technical direction, developing team capabilities, and integrating and prioritizing projects, primarily focused on machine-learning applications.
Develop and execute a roadmap for implementing state-of-the-art AI/ML approaches that use Septerna’s GPCR structural data to discover and refine novel chemical matter.
Identify productive emerging technologies and lead their integration into Septerna’s computational stack through internal development, strategic partnerships, or both.
Oversee and extend analysis pipelines, including DNA-encoded library (DEL), high-throughput screening (HTS), and assay-data workflows; identify opportunities to advance Data Science as part of 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 and establish best practices for data curation and model validation.
Collaborate closely with Computational Chemistry, Structural Biology, and other cross-functional research teams to accelerate hypothesis generation and scientific discovery.
Serve as a senior technical lead for cross-functional initiatives and use technical insight and scientific judgment to guide research strategy.