Research Scientist, Virtual Cell Modelling & Perturbative Biology Foundation Models
Core
Building multimodal foundation models to predict cellular responses to chemical and genetic perturbations using petabyte-scale omics and imaging data to replace or augment wet-lab screens.
Role type
Research Scientist (ML for Drug Discovery)
Builds
Virtual cells and in silico prediction frameworks for drug discovery
Domain
Biotechnology / Machine Learning / Drug Discovery
Deliverable
production ML models
Required skills
Generative modeling (flow matching, diffusion), representation learning, Python, high-performance compute engineering, scientific publishing, cross-functional collaboration
Preferred skills
Biological data experience, compiled languages, perturbational experimental paradigms knowledge
Technologies
Python, high-performance compute clusters
Responsibilities
Develop generative and distributional models for high-dimensional cellular responses; Build and maintain ML systems for massive multiomics datasets; Ensure model predictions are interpretable and grounded in experimental outcomes; Design and implement rigorous evaluation metrics for generalization; Publish findings in top-tier venues
Seniority
Senior, hands-on IC
