Principal Scientist, Portfolio & gRED Interface - Biologics, AI for Drug Discovery

About the role

Roche's AI for Drug Discovery (AIDD) group, part of the Computational Sciences Center of Excellence, is looking for a Principal Scientist to bridge its computational methods with active antibody discovery projects at Genentech (gRED). The role applies machine learning models to portfolio projects, advises gRED leadership on computational strategy and builds a team of computational scientists.

Key responsibilities

• Lead the application of machine learning models (developability, functional modeling, design optimization) to active gRED projects, taking models from research to projects with measurable outcomes
• Serve as a trusted technical advisor to gRED leadership on computational strategy, capability development and portfolio prioritization, and act as the main liaison between computational sciences and gRED
• Lead the Large Molecule portfolio modeling team, set research direction, mentor scientific development and build a collaborative environment
• Develop and execute a technical strategy for how ML shapes antibody discovery and engineering, aligned with the pRED counterpart
• Navigate stakeholder groups such as Antibody Engineering, platform teams and external partners to align on priorities
• Identify and own high-impact research initiatives that advance the portfolio and the science

Required profile

• PhD in Computational Biology, Biophysics, Immunology, Chemistry or Computer Science with 10+ years of drug discovery experience, including 5+ in ML/computational methods for biologics
• Expertise in computational methods for large molecule design, with depth in antibody engineering, biophysical modeling or developability assessment
• Track record of developing novel computational methods that influenced real drug discovery projects, and first-author publications in computational drug discovery
• Proven ability to translate computational research into portfolio impact, with experience on active projects influencing decisions and project prioritization
• Experience leading research teams or mentoring junior scientists, and strong communication skills with senior leadership

Required skills

• Machine learning and computational methods for biologics
• Biophysical modeling and developability assessment
• Understanding of the antibody discovery lifecycle: target selection, lead optimization, humanization and development

Expected salary range for California: $201,300 - $373,800, with a possible discretionary annual bonus. Relocation benefits are not available.