AI Scientist/Senior, Clinical & Molecular Genomics Modeling, BRAID

Roche • South San Francisco, CA

23h

About The Position

Roche's Research and Early Development organisations (gRED and pRED) leverage advances in AI, data, and computational sciences to accelerate R&D. The new Computational Sciences Center of Excellence (CoE) aims to harness data and AI to deliver innovative medicines. The Biological Research | AI Development (BRAID) team within AI Biology & Translation (AIBT) focuses on developing state-of-the-art AI methods for disease biology, target discovery, and translational research. This role is for a Scientist/Senior Scientist with a strong background in computational, statistical, and data science, and a drive to translate technical advancements into biological and clinical impact. The successful candidate will develop and define modeling applications to empower clinical trials, requiring a deep understanding of how models integrate into Genentech’s drug development lifecycle, including identifying users, data availability timelines, and decision-making processes. The role involves close collaboration with clinical and translational research colleagues to ensure models directly improve decision-making.

Requirements

• Ph.D. in Computer Science, Bioinformatics, Computational Biology, or a related quantitative field.
• Proven experience building machine learning and AI models from scratch.
• Hands-on experience working with "messy" genomics data (e.g., aggregating dozens or hundreds of studies) and/or clinical datasets.
• Ability to effectively use agentic coding tools to improve the quality and quantity of code and modeling outputs.
• For Senior AI Scientist: 2 years of experience building ML models and/or interpreting models for target discovery, biomarker discovery, or clinical decision making (post-Ph.D.).
• For Senior AI Scientist: Expertise within an area of modern machine learning research, such as graph/diffusion/transformer models, reinforcement learning, or multimodal representation learning.

Responsibilities

• Develop innovative models to enhance insights from outcome data and design interpretable ML frameworks.
• Create models that link heterogeneous molecular and cellular data with clinical outcomes, specifically focusing on prognostic, predictive, and pharmacodynamic biomarkers.
• Integrate messy, heterogeneous data from internal and public cohorts into a unified, generalizable modeling framework.
• Identify necessary data collection requirements for future trial designs to ensure modeling success.
• Share and deploy these models with other computational users and clinical stakeholders to drive impact.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees