Senior Machine Learning Scientist

About the role

Roche's Frontier Research team within its Computational Sciences Center of Excellence is looking for a Senior Machine Learning Scientist in Basel. The team focuses on foundational machine learning research and new algorithmic frameworks, especially generative modeling, to help scientists in the Pharma and Genentech early development organizations turn data and AI into better medicines. The position sits in a flat scientific structure where senior scientists set their own research agenda.

Key responsibilities

• Design, implement and benchmark novel machine learning algorithms based on causal representation learning and out-of-distribution generalization to solve complex biological problems.
• Frame foundational research questions beyond off-the-shelf ML approaches and develop robust, deployable architectures for drug discovery.
• Collaborate with cross-functional research teams across the global innovation hubs in Basel, New York and San Francisco.
• Build, experiment with and scale unconventional neural network architectures using standard deep learning frameworks.
• Translate biological challenges into rigorous computational models that bridge machine learning theory and clinical impact.
• Author and drive high-impact publications to top-tier machine learning and computational biology venues.
• Present research at internal symposia and international conferences, and take part in code reviews and technical discussions.

Required profile

• Currently pursuing a Ph.D. (or an exceptional Master's degree) in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics or a closely related field, with strong practical research experience.
• Solid grounding in machine learning theory, with practical familiarity or strong interest in causal representation learning and/or out-of-distribution generalization.
• Demonstrated track record of scientific contributions, such as publications or accepted manuscripts at premier machine learning conferences or journals (e.g. NeurIPS, ICML, ICLR, AISTATS, UAI, CVPR, ACL).
• Interest in biological datasets and molecular systems; prior direct biological experience is an asset but not mandatory.
• Excellent collaboration skills and ability to communicate complex technical ideas across multi-disciplinary, distributed teams.

Required skills

• Hands-on experience building, training and troubleshooting deep neural networks.
• Proficiency in modern deep learning frameworks (PyTorch strongly preferred).