Senior Machine Learning Scientist

Description

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

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. ​

Frontier Research is dedicated to foundational machine learning research and developing new algorithmic frameworks. We operate with a flat scientific structure in which senior scientists define their own research agendas, and leaders act as mentors who shape priorities across the organization. We view open science as a core value and a competitive necessity. Our commitment to open dissemination, academic engagement, and community contribution ensures that our work contributes meaningfully to the broader machine learning community and advances the scientific ecosystem. In biology, many exciting research questions cannot yet be addressed with off-the-shelf ML approaches—they demand not only novel solutions but also new ways of framing the questions themselves, often beyond existing ML paradigms. We believe that the field of generative modeling provides the most promising paths to connect these fields and build robust, impactful solutions.

The Opportunity:

Design, implement, and benchmark novel machine learning algorithms grounded in causal representation learning and out-of-distribution generalization to address complex biological problems.

Frame foundational research questions that transcend off-the-shelf ML paradigms, creating robust, deployable architectures tailored to drug discovery.

Partner and collaborate closely with cross-functional research teams across our 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 abstract biological challenges into rigorous computational models capable of bridging the gap between machine learning theory and clinical impact.

Author and drive high-impact scientific publications for submission to top-tier machine learning and computational biology venues.

Present research findings and represent the Frontier Research team at both internal symposia and leading international scientific conferences.

Actively engage in knowledge-sharing, code reviews, and technical discussions that elevate our computational capabilities and foster an open, innovative team dynamic.

Requirements

• 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, with strong practical research experience.
• 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 (prior direct biological experience is an asset, but not mandatory).
• 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.
• Ready to bring your unique qualities to Roche and make an impact? Apply now and join us in our mission to shape the future of healthcare.
• 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.