Machine Learning Scientist/Senior Machine Learning...

A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

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.

• 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.