Director, Machine Learning, Virtual Cell Initiative
Core
Lead the development of predictive machine learning models for perturbative gene expression modeling within a full-stack virtual cell initiative to identify disease mechanisms and nominate drug targets.
Role type
Director, Machine Learning (Virtual Cell Initiative)
Builds
State-of-the-art foundation models and agentic frameworks for understanding cellular responses to perturbations
Domain
Artificial Intelligence and Biology (AIxBio), specifically single-cell genomics and virtual cell modeling
Deliverable
production ML models
Required skills
Machine learning leadership, single-cell genomics expertise, perturbative gene expression modeling, multimodal model development, team building and management, cross-functional collaboration with experimental biologists, frontier ML research
Preferred skills
Experience with PyTorch, TensorFlow, JAX, ability to recruit and mentor scientists and engineers
Technologies
PyTorch, TensorFlow, JAX
Responsibilities
Lead a team of 6 ML research scientists and students to build a foundation model for cell perturbation responses; collaborate with wet lab scientists to shape large-scale single-cell training datasets; integrate genomics, functional track, and omics data beyond scRNA-seq and Perturb-seq; pioneer new ML architectures; attract top talent to the initiative
Seniority
Director, hands-on IC with team leadership
