Senior Machine Learning Scientist

## The Position 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. 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. 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. ## 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. ## Who You Are - 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.