Senior Scientist, Antibody Developability & Biophysics (AIDD)

About the role

Roche's AI for Drug Discovery (AIDD) group, part of the Computational Sciences Center of Excellence, is building computational approaches that accelerate antibody discovery. The role applies developability prediction, design optimization and functional modeling to active portfolio projects in Genentech (gRED) and Pharma (pRED), working directly with scientists to turn machine learning models into portfolio impact. The expected salary range for this position, based on the primary location of California, is $168,100 - $312,300, with a discretionary annual bonus possible.

Key responsibilities

• Apply developability modeling to active portfolio projects in gRED and pRED, turning computational predictions into guidance for antibody engineering teams.
• Support the continuation and evolution of molecular assessment research with Antibody Engineering stakeholders.
• Interface with portfolio scientists to understand their challenges and translate them into computational approaches, communicating model outputs and limitations clearly.
• Contribute to method development in developability prediction, biophysical modeling and ML model improvement.
• Work with modeling and platform teams to move research-stage models into production-ready components.
• Build technical credibility with gRED/pRED stakeholders as a trusted resource for computational support in antibody design.

Required profile

• PhD in Computational Biology, Biophysics, Chemistry or a related field, or equivalent advanced experience (5-8 years in ML/computational methods or biophysics).
• Strong expertise in developability assessment, biophysical modeling or antibody engineering, with a deep understanding of what makes antibodies druglike.
• Experience on drug discovery projects where computational work influenced scientific decisions, and familiarity with the practical constraints of portfolio science.
• First-author publications or equivalent evidence of research contributions.
• Ability to explain complex computational methods to non-experts and to collaborate across disciplines.
• Ambition to grow into broader technical and/or leadership roles.

Required skills

• Python and machine learning frameworks such as PyTorch, TensorFlow or JAX.
• Molecular modeling tools, biophysical analysis or related computational approaches.