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Senior Machine Learning Scientist

Roche · Schweiz · Full time · Onsite

About this role

Senior Machine Learning Scientist at Roche’s Computational Sciences Center of Excellence in Basel, leading foundational ML research for drug discovery. The role focuses on causal representation learning, out‑of‑distribution generalization, and generative modeling to solve complex biological problems. It suits a Ph.D. or exceptional Master’s candidate with deep learning expertise and a strong publication record.

Key details

• Location: Schweiz
• Level: Senior

What you'll do

• Design, implement, and benchmark novel machine learning algorithms grounded in causal representation learning and out‑of‑distribution generalization.
• Frame foundational research questions that transcend off‑the‑shelf ML paradigms and create deployable architectures for drug discovery.
• Partner and collaborate closely with cross‑functional research teams across Basel, New York, and San Francisco.
• Build, experiment with, and scale unconventional neural network architectures using standard deep learning frameworks.
• Translate abstract biological challenges into rigorous computational models bridging ML theory and clinical impact.
• Author and drive high‑impact scientific publications for top‑tier ML and computational biology venues.
• Present research findings at internal symposia and leading international scientific conferences.
• Engage in knowledge‑sharing, code reviews, and technical discussions to elevate computational capabilities.

What you'll need

• Currently pursuing a Ph.D. (or exceptional Master’s degree) in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics, or a closely related technical field.
• Solid grounding in machine learning theory and principles, with practical familiarity or strong interest in causal representation learning and/or out‑of‑distribution generalization.
• Hands‑on expertise in building, training, and troubleshooting deep neural networks, with demonstrated proficiency in modern deep learning frameworks (PyTorch strongly preferred).
• Demonstrated track record of scientific contribution, evidenced by publications or accepted manuscripts at premier machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, AISTATS, UAI, CVPR, ACL).
• Exposure to or strong interest in working with biological datasets and molecular systems.
• Outstanding collaborative and interpersonal skills, capable of communicating intricate technical ideas clearly across multi‑disciplinary, distributed global teams.
• Driven by curiosity, unconventional problem‑solving, and a commitment to core values of Integrity, Courage, and Passion.

Tech stack

PyTorch, deep learning frameworks, causal representation learning, generative modeling

How to stand out for this role

Likely interview topics

• Designing causal representation learning algorithms for biological data
• Building and scaling unconventional neural network architectures in PyTorch
• Translating biological challenges into computational models
• Collaborating across global, multidisciplinary teams
• Publishing and presenting research in top-tier venues

Keywords to mirror in your CV

Ph.D., machine learning theory, causal representation learning, deep learning frameworks, PyTorch, NeurIPS, ICML, ICLR, AISTATS

What to show

• Repository of a novel ML algorithm with performance benchmarks on biological datasets
• Demo of a generative model applied to molecular system data
• Paper or preprint showcasing causal representation learning applied to drug discovery

How to apply

Use the "Apply on company website" button to continue to Roche's careers page and submit your application there.

Source: Adzuna