Senior Machine Learning Scientist, Foundational ML, AI for Biology & Translation

About the role

Roche's Genentech Research and Early Development (gRED) is seeking a Senior Machine Learning Scientist to join the Foundation Models team (DELTA) in the AI for Biology & Translation (AIBT) department. The team advances AI research in drug and target discovery, focusing on large-scale foundation models in biology, and works alongside ML scientists, ML engineers and computational biologists.

Key responsibilities

• Design and build foundation models for target and drug discovery, covering large-scale representation learning, multimodal generative models, LLMs, AI agents and reinforcement learning.
• Work with and integrate diverse data modalities such as molecular structures, biological sequences, omics data, biochemical readouts and text.
• Bridge AI models and applications supporting target discovery, experimental design and lab-in-the-loop pipelines.
• Scale frontier AI models to massive datasets, collaborating with engineering and MLOps teams on system design, architecture and scalability.
• Publish in top-tier ML venues and scientific journals and present results at internal and external conferences and workshops.
• Collaborate closely with interdisciplinary teams across gRED and Roche.

Required profile

• Ph.D. in Computer Science, Machine Learning, Computational Biology or a related quantitative field.
• 0-2+ years of industry or post-doc experience.
• Proven track record advancing ML models in research and/or industry, especially in large-scale representation learning, multimodal generative models, LLMs, AI agents and reinforcement learning.
• Demonstrated interest in AI for biology, chemistry and drug discovery.
• Strong publication record in top-tier ML venues (NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, AISTATS, etc.) or a public portfolio of projects.

Required skills

• Excellent knowledge of deep learning theory and practice.
• Excellent Python programming, fluency with modern agentic coding environments, and experience with PyTorch or JAX.
• Software engineering, data engineering and MLOps best practices (version control, high-performance compute, experiment monitoring).
• Preferred: hands-on experience with molecular structures, single-cell/omics data, perturbation biology, multimodal biological datasets, and developing, finetuning and optimizing LLMs and agentic systems.

Salary range for the San Francisco location is $147,800 - $274,400, with a possible discretionary annual bonus. Relocation benefits are not available.