onsite
Machine Learning Scientist - Synthesis Planning and Optimization - Genentech
ML Engineer
Machine Learning Scientist focused on synthesis‑aware molecular design, developing generative and retrosynthetic models to create drug‑like compounds that are synthetically feasible using Python and deep learning frameworks.
About the role
Key Responsibilities
- Design and implement machine‑learning algorithms for retrosynthesis prediction, synthesis planning, and generation of synthetically accessible molecules.
- Integrate proprietary reaction data and biochemical constraints into generative models to close the loop between design and automated synthesis.
- Develop and evaluate deep‑learning architectures (e.g., graph neural networks, transformer models) using Python libraries such as PyTorch and RDKit.
- Collaborate with chemists and automation engineers to validate computational predictions on real‑world synthesis platforms.
- Publish research findings and contribute to open‑source tools that advance AI‑driven drug discovery.
Requirements
- Ph.D. or equivalent experience in Machine Learning, Computational Chemistry, or a related field.
- Strong programming skills in Python and experience with deep‑learning frameworks (PyTorch, TensorFlow).
- Hands‑on experience with cheminformatics tools (RDKit) and knowledge of retrosynthetic analysis.
- Proven track record of developing and deploying ML models for chemical or biological data.
- Excellent problem‑solving abilities and ability to work cross‑functionally with experimental scientists.
Skills
pythonmachine learningdeep learningpytorch