Senior Machine Learning Scientist

Jobtailor; California (MO); On-site; USD 140,000 - 220,000; Full time

Job summary

Roche/Genentech seeks a PhD-level ML leader to design and evolve scientific reasoning systems for drug discovery. You will define architectures, training strategies, and evaluation methodologies, translating domain knowledge into actionable ML objectives and robust training code.

You will build scalable distributed ML pipelines, collaborate with researchers, and mentor junior staff while ensuring production-readiness and rigorous benchmarks across experiments.

Qualifications

• PhD in Computer Science, Statistics, Mathematics, Physics, or related quantitative field.
• 0–2+ years of industry or post-doc experience with 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

Responsibilities

• 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

Skills

Deep Learning Experience

Large-Scale Machine Learning Systems

Software Engineering Skills

Mentoring

Collaboration

Communication

Research Excellence

Education

PhD in Computer Science, Statistics, Mathematics, Physics, or related quantitative field

0–2+ years of industry or post-doc experience with focus on deep learning

Job description

• 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

Requirements

• 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

Core Competencies

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.

Highest-signal resume keywords

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

Hard 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

Soft Skills

• Mentoring
• Collaboration
• Communication

Industry Keywords

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