Senior Scientist, Antibody Developability/Biophysics & Portfolio Support, AI for Drug Discovery (AIDD)

Job description

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. ​ The Opportunity At Roche's AI for Drug Discovery (AIDD) group within the Computational Sciences Center of Excellence, we are building computational approaches that accelerate antibody discovery. Our focus is on translating machine learning models into portfolio impact—working directly with gRED and pRED scientists to apply developability prediction, design optimization, and functional modeling to real projects. We're looking for a talented Senior Scientist who combines strong technical expertise in developability and biophysics with the ability to collaborate effectively across diverse scientific teams. In this role, you'll work on active portfolio projects, apply cutting-edge computational methods to real discovery challenges, and grow into broader leadership as you advance. Antibody design is becoming increasingly computational. The scientists who can bridge computational methods and experimental reality—who understand both the science deeply and the practical needs of portfolio teams—are increasingly valuable. This role gives you the opportunity to have direct impact on real projects while building expertise in a rapidly evolving field. In this role, you will: Apply developability modeling to active portfolio projects in gRED and pRED, translating computational predictions into actionable guidance for antibody engineering teams Support the continuation and evolution of our molecular assessment research efforts in partnership with Antibody Engineering stakeholders, ensuring continuity and quality of ongoing work Interface with portfolio scientists and stakeholders to understand their scientific challenges and translate those into computational approaches; clearly communicate model outputs and limitations Contribute to method development in developability prediction, biophysical modeling, and ML model improvement—identifying gaps in our current approaches and proposing solutions Work with our modeling and platform teams to transition research-stage models into production-ready components that can be reliably used on portfolio projects Develop technical relationships and credibility with gRED/pRED stakeholders, establishing yourself as a trusted resource for computational support in antibody design Who You Are Technical Foundation PhD in Computational Biology, Biophysics, Chemistry, or 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 ; deep understanding of what makes antibodies druglike (expression, stability, biophysical properties, manufacturability) Proficiency in Python and machine learning frameworks (PyTorch, TensorFlow, or JAX) Experience with molecular modeling tools, biophysical analysis, or related computational approaches First-author publications or equivalent evidence of research contributions Drug Discovery & Portfolio Experience Experience working on drug discovery projects where your computational work directly influenced scientific decisions Understanding of antibody engineering, developability assessment, and the full lifecycle of antibody optimization Ability to explain complex computational methods to non-expert audiences Familiarity with the practical constraints and timelines of portfolio science Collaboration & Communication Strong communication skills; you can present complex science clearly and collaborate across disciplines You enjoy working with experimental scientists and translating their challenges into computational problems You're responsive to stakeholder needs and pragmatic about what's feasible You bring energy and curiosity to your work Growth Potential You have ambition to grow into broader technical and/or leadership roles You're willing to take on ambiguous problems and figure them out You seek feedback and are committed to continuous improvement Relocation benefits are NOT available for this job posting The expected salary range for this position, based on the primary location of California, is $ 168,100 - 312,300. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below. . Benefits #ComputationCoE #tech4lifeComputationalScience #tech4lifeAI Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws. If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants .