Genentech - San Francisco, CA
posted 2 months ago
Genentech seeks a highly motivated Senior ML Scientist to join the Cellular States and their Organization group within the BRAID (Biology Research - AI Development) department in Genentech Research and Early Development (gRED). Our group is committed to designing and implementing transformative Machine Learning capabilities to model and probe the cellular biology of disease, from DNA variation to tissue organization, and how it is manifested in the patient populations we want to impact. These underpin our goal to revolutionize how we discover and characterize novel therapeutic targets and the clinical contexts in which they can be most safe and efficacious. This way, we intend for state (and future)-of-the-art Machine Learning to have material and positive societal impact. In this position, you will lead the development and application of next-generation ML methods for the representation, analysis, and interpretation of spatial genomics data and its integration with other imaging and sequencing data modalities. Successful candidates are expected to bring significant experience with single-cell and spatial data, demonstrable experience of thoughtfully implementing modern neural network models for biological applications, and a deep curiosity for how these will impact therapeutic and target discovery. You will work closely with an existing team of software developers, machine learning experts, computational biologists, and wet lab collaborators to develop multimodal models of multi-cellular communities in their tissue contexts. You will immediately impact key ongoing and planned projects in diverse therapeutic areas including Neuroscience, Immunology, Cancer Immunology, and Molecular Oncology. You will lead the application of cutting-edge ML models to address bottlenecks in target discovery and drug development. You will collaborate closely with different therapeutic areas and cross-functional teams in gRED to understand needs and concerns. You will provide domain-specific input to the development of foundation models. You will scale ML models to large biological datasets, working at the intersection of deep learning and engineering challenges to support new scientific questions. You will regularly publish in top-tier ML, computational biology venues and/or scientific journals, presenting results at internal and external scientific venues, conferences, and workshops.