Senior / Principal Machine Learning Scientist, Scientific Reasoning Models, AI for Drug Discovery

About the Role

Roche's AI for Drug Discovery (AIDD) group (Prescient Design) is revolutionizing drug discovery with cutting-edge machine learning techniques. We are seeking a Senior or Principal Machine Learning Scientist to join the Foundation Models team within Prescient Design (gRED). In this role, you will drive the development of our internal reasoning Large Language Models (LLMs) and enable it to succeed at drug discovery tasks, including biomolecular design. You will work at the intersection of engineering and research, designing and scaling large machine learning systems.

Responsibilities

• Technical Leadership & Strategy: Lead the design and evolution of scientific reasoning systems, setting technical direction for model architectures, training strategies, and evaluation methodologies.
• Model Capability & Improvement: Define and execute approaches to systematically improve model performance on scientific tasks, including long-horizon reasoning and complex decision-making.
• Domain Translation: Translate biological and chemical domain knowledge into machine learning objectives, training signals, and evaluation criteria, working closely with domain experts.
• Scalable Systems & Engineering: Architect and improve large-scale distributed machine learning systems, ensuring robustness, efficiency, and reproducibility across training and evaluation workflows.
• Research-to-Production Impact: Partner with researchers and cross-functional teams to move models from research prototypes to production-ready systems that support active discovery programs.

For Senior ML Scientist

• Primary driver of 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.
• Act as a technical mentor to junior staff and interns, fostering a culture of engineering excellence and rapid experimentation.

For Principal ML Scientist

• Help define the long-term technical roadmap for scientific reasoning models, identifying new opportunities and setting priorities across initiatives.
• Architect new initiatives that integrate diverse data modalities, guiding the technical direction of cross-functional projects across gRED.
• Serve as a key technical authority for leadership, influencing how Genentech leverages generative AI to solve high-stakes problems in the therapeutic pipeline.

Qualifications

• PhD in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field.
• For Senior (SE6): 0 – 2+ years of industry or post-doc experience with a focus on deep learning.
• For Principal (SE7): 5+ years of industry or post-PhD experience with a demonstrated track record of technical leadership and project ownership.
• Extensive experience developing and training large-scale machine learning models, including approaches to improve domain understanding, reasoning capabilities, and model alignment.
• A 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.

Preferred Experience

• 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.

Pay

San Francisco primary location: Senior Scientist: $167,400 - 310,800; Principal Scientist: $194,000 - 360,400.
New York City primary location: Senior Scientist: $160,100 - 297,300; Principal Scientist: $185,600 - 344,600.
Actual pay determined by 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.