Latinx in AI (LXAI)
Machine Learning Scientist - Small Molecule Drug Design
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At Roche, you can be yourself and will be valued for your unique qualities. Our culture fosters personal expression, open dialogue, and genuine connections. Here, you will be appreciated, accepted, and respected for who you are. This creates an environment where you can grow both personally and professionally. Together, we want to prevent, stop, and cure diseases and ensure that everyone has access to healthcare – today and in the future. Become part of Roche, where every voice counts.
The Position
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 Small Molecule Drug Design. You will develop and apply ML methods and models to accelerate small-molecule drug design with a focus on structure-driven approaches, working hand in hand with world-class computational and medicinal chemists and structural biologists.
The Opportunity
- Design, build, and apply cutting-edge ML models for small-molecule drug design, focused on protein–ligand interactions, binding affinity, and key molecular properties.
- Train and fine-tune foundation models for structure prediction, using internally developed and open-source models on internal datasets.
- Validate and refine ML-generated hypotheses alongside world-class computational and medicinal chemists and structural biologists.
- Drive scientific impact through publications, open-source releases, and conference talks.
- Collaborate widely with computational and experimental researchers at Roche and with academic partners.
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Who You Are
- You bring deep machine-learning expertise with a strong foundation in linear algebra, probability and optimization, and hands-on experience designing & implementing machine learning approaches such as graph neural networks, sequence models, and reinforcement learning.
- You have structure-driven modeling experience with co-folding methods, binding-affinity prediction, and structural-biology datasets.
- You are fluent in Python and cheminformatics with toolkits such as RDKit and OpenEye, and modern ML frameworks like PyTorch or JAX.
- 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
- Hands-on experience building structure-prediction foundation models.
- Experience collaborating directly with medicinal chemists and structural biologists.
Location & Travel Requirements
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This position must be based in either Basel, Switzerland (preferred) or Welwyn Garden City in the United Kingdom. Although this can be a hybrid role, there is an expectation of ongoing and sustained site presence, in compliance with local company site requirements.
If using AI to design the medicines patients need next inspires you, apply now and help accelerate small-molecule discovery at Roche.
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Where pay transparency applies, details are provided based on the primary posting location. For this role, the primary location is Basel. If you are interested in additional locations where the role may be available, we will provide the relevant compensation details later in the hiring process.
Who We Are
A healthier future drives us to innovate. More than 100,000 employees worldwide work together to advance science and ensure everyone has access to healthcare – today and for future generations. Through our commitment, over 26 million people are treated with our medicines and more than 30 billion tests are performed with our diagnostic products. We encourage each other to explore new possibilities, foster creativity, and set ambitious goals to deliver life-changing healthcare solutions.
Together we can create a healthier future.
Roche is an employer that promotes equal opportunities.
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