Director, Machine Learning, Virtual Cell Initiative

Role: Lead the development of machine learning models based on single-cell genomic data within Arc's Virtual Cell Initiative, aiming to create a predictive, biology-inspired virtual cell model for drug discovery. Responsibilities: build and manage a team of 6 ML scientists and engineers, collaborate with wet lab biologists to integrate diverse omics data, pioneer novel ML architectures, and contribute to foundational model advancements for understanding cellular responses to perturbations. Qualifications: PhD in Computational Biology, Bioinformatics, Machine Learning, or related fields; 5+ years of ML experience with frameworks like PyTorch, TensorFlow, or JAX; proven leadership in multidisciplinary research environments; strong communication skills and biological interest. High-Value: focus on single-cell genomics, perturbation modeling, active learning, precision virtual cell modeling; collaborative environment with experimental and computational integration; headquarters in Palo Alto with a flexible work setup. WorkSetup: not explicitly specified, but implies an active, research-focused environment in Palo Alto.