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# Machine Learning Scientist - Synthesis Planning...

- **Company:** Genentech
- **Location:** South San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $167,400.0 - $310,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, Github, Python (Programming Language), Machine Learning, Language Modeling, Tensorflow, Software Engineering, Reinforcement Learning, Planning Software, Pytorch, Gitlab
- **Published:** June 21, 2026
- **Apply:** https://www.juju.com/job/00000000g9xfdo

## About the Role

+ You bring deep machine-learning expertise with a strong foundation in linear algebra, probability and optimization, and hands-on experience in modern machine learning approaches such as graph-neural networks, sequence/language models and reinforcement learning.

 + You are familiar with chemistry concepts relevant to synthesis planning and molecular optimisation as well as small molecule data and cheminformatics toolkits such as RDKit or Openeye.

 + You are fluent in Python and have experience with modern ML frameworks like PyTorch or JAX as well as scientific software development.

 + You hold a PhD or equivalent research depth in machine learning, computational chemistry, chemical engineering or a related quantitative field such as physics or statistics.

 + You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab)..

Preferred:

 + Experience with retrosynthesis or synthesis-planning models.

 + Experience with automated/high-throughput synthesis.

## Description

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.

The Opportunity:

 + **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.


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