Synthesis‑Aware ML Scientist for Drug Discovery

NLP PEOPLE; San Francisco (CA); On-site; USD 147,600 - 274,000; Full time

Job summary

Genentech is seeking a Machine Learning Scientist to develop synthesis‑aware molecular design methods and to integrate proprietary reaction data for hit finding and optimisation. You will build scalable pipelines interfacing with automated synthesis platforms and collaborate across computational and experimental teams.

The role emphasizes strong ML foundations, experience with graph networks and reinforcement learning, and a track record of publications or public projects.

Qualifications

• Deep machine‑learning expertise with strong foundation in linear algebra, probability, and optimization.
• Familiarity with chemistry concepts relevant to synthesis planning and molecular optimisation.
• Fluent in Python and experience with ML frameworks like PyTorch or JAX.
• PhD or equivalent research depth in ML/chemistry/engineering with up to 2 years industry experience (Scientist) or 2+ years (Senior Scientist).
• Publications or public portfolio of relevant projects (GitHub/GitLab).

Responsibilities

• Develop and advance ML methods for synthesis‑aware molecular design across retrosynthesis, synthesis planning, molecular generation, and search.
• Integrate proprietary reaction and biochemical data to design next‑gen synthesis‑aware models and workflows.
• Build robust, scalable pipelines for active‑learning loops interfacing with automated synthesis platforms.
• Design novel batch synthesis‑planning algorithms to maximise chemical space coverage and information gain.
• Drive scientific impact through publications, open‑source releases, and conference talks.
• Collaborate with computational and experimental researchers at Roche and with academic partners.

Skills

Deep ML expertise

Graph neural networks

Sequence models

Reinforcement learning

Python proficiency

Education

PhD in ML/comp chem/related field

Tools

RDKit

Openeye

PyTorch

JAX

Job description

Genentech is seeking a Machine Learning Scientist to develop synthesis‑aware molecular design methods and to integrate proprietary reaction data for hit finding and optimisation. You will build scalable pipelines interfacing with automated synthesis platforms and collaborate across computational and experimental teams.

The role emphasizes strong ML foundations, experience with graph networks and reinforcement learning, and a track record of publications or public projects.