Machine Learning Scientist - Synthesis Planning and Optimization
Genentech•Daly City, CA
•$147,600 - $310,800
About The Position
Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech’s Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist in Synthesis Planning and Optimization. You will build ML methods that design molecules we can actually make — closing the loop between generative design and automated synthesis.
Requirements
- Deep machine-learning expertise with a strong foundation in linear algebra, probability and optimization.
- Hands-on experience in modern machine learning approaches such as graph-neural networks, sequence/language models and reinforcement learning.
- Familiarity with chemistry concepts relevant to synthesis planning and molecular optimisation as well as small molecule data and cheminformatics toolkits such as RDKit or Openeye.
- Fluent in Python and experience with modern ML frameworks like PyTorch or JAX as well as scientific software development.
- Hold a PhD or equivalent research depth in machine learning, computational chemistry, chemical engineering or a related quantitative field such as physics or statistics.
- A record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab).
Nice To Haves
- Experience with retrosynthesis or synthesis-planning models.
- Experience with automated/high-throughput synthesis.
Responsibilities
- Develop and advance machine learning methods for synthesis-aware molecular design across retrosynthesis, synthesis planning, molecular generation, and search in synthesizable chemical spaces.
- Integrate proprietary reaction and biochemical data to design the next generation of synthesis-aware models and workflows for hit finding and optimisation.
- Build robust, scalable pipelines for active-learning loops that interface directly with automated and high-throughput synthesis platforms.
- Design novel batch synthesis-planning algorithms that maximise chemical-space coverage, information gain and experimental efficiency.
- Drive scientific impact through publications, open-source releases, and conference talks.
- Collaborate widely with computational and experimental researchers at Roche and with academic partners.
Benefits
- Discretionary annual bonus may be available based on individual and Company performance.
- Benefits detailed at the link provided below.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
