Senior Machine Learning Scientist

• Lead the design and evolution of scientific reasoning systems, setting technical direction for model architectures, training strategies, and evaluation methodologies
• Define and execute approaches to systematically improve model performance on scientific tasks, including long-horizon reasoning and complex decision-making
• Translate biological and chemical domain knowledge into machine learning objectives, training signals, and evaluation criteria, working closely with domain experts
• Architect and improve large-scale distributed machine learning systems, ensuring robustness, efficiency, and reproducibility across training and evaluation workflows
• Partner with researchers and cross-functional teams to move models from research prototypes to production-ready systems that support active discovery programs
• Drive technical implementation for scientific reasoning, translating high-level research goals into robust training code
• Own the end-to-end integrity of large-scale training runs, from data orchestration to the development of rigorous reasoning benchmarks
• Mentor junior staff and interns, fostering engineering excellence and rapid experimentation

• PhD in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field
• 0–2+ years of industry or post-doc experience with a focus on deep learning
• Extensive experience developing and training large-scale machine learning models, including approaches to improve domain understanding, reasoning capabilities, and model alignment
• Strong history of research excellence at top-tier venues (e.g., NeurIPS, ICLR, ICML)
• Strong software engineering skills and experience designing and operating large-scale or high-performance machine learning systems
• Experience with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data) is highly valued but not required
• A public portfolio of research or significant contributions to open-source ML libraries
• A passion for applying frontier AI to drug discovery
• Onsite presence on our campus is expected in compliance with Roche/Genentech policy
• Relocation benefits are not available for this job posting

Demonstrates expertise in designing and evolving scientific reasoning systems, with a strong focus on large-scale machine learning model development and training. Capable of translating complex biological and chemical knowledge into actionable machine learning objectives while mentoring junior staff and fostering a culture of engineering excellence.

• PhD In Computer Science
• Deep Learning Experience
• Large-Scale Machine Learning Systems
• Research Excellence In NeurIPS
• Software Engineering Skills

• Machine Learning Model Development
• Model Training Strategies
• Evaluation Methodologies
• Data Orchestration
• Reasoning Capabilities
• Model Alignment
• Training Code Implementation
• Robustness And Efficiency
• Molecular Modalities
• Research Contributions

• Mentoring
• Collaboration
• Communication

• Scientific Reasoning Systems
• Drug Discovery
• Quantitative Field
• Biological Knowledge
• Chemical Knowledge