Machine Learning Scientist - Small Molecule Drug Design

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

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.

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 modelling 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 PyTorchor 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

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. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.