Machine Learning Scientist - Synthesis Planning and Optimization

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.
• Drive scientific impact through publications, open-source releases, and conference talks.
• Collaborate widely with computational and experimental researchers at Roche and with academic partners.
  
  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.