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Machine Learning Scientist – Synthesis Planning and Optimization
RocheMachine Learning Scientist developing advanced methods for synthesis-aware molecular design at Roche. Collaborating on innovative approaches in the AI-driven drug discovery process.
Posted 6/20/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSenior💰 $147,600 - $310,800 per yearWebsite
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- Deep machine-learning expertise
- 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
- Familiarity with small molecule data and cheminformatics toolkits such as RDKit or Openeye
- Fluency in Python
- Experience with modern ML frameworks like PyTorch or JAX
- Scientific software development experience
- PhD or equivalent research depth in machine learning, computational chemistry, chemical engineering, or a related quantitative field such as physics or statistics
- Record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects
Benefits
Comp & perks- Discretionary annual bonus based on individual and Company performance
- Benefits detailed at the link provided
ATS Keywords
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Hard Skills & Tools
machine learninglinear algebraprobabilityoptimizationgraph-neural networkssequence modelsreinforcement learningPythonscientific software developmentmolecular optimisation
Soft Skills
collaborationcommunicationscientific impact
Certifications
PhD