You will lead and execute a research program that develops novel foundational deep-learning methods with the ultimate aim of contributing to the drug discovery process. You will work in an exciting and multidisciplinary environment alongside AI scientists, ML engineers, computational biologists, and biological chemists, working on different areas of biology and chemistry. You will lead the research, design, and execution of novel, cutting-edge ML research with applications to drug discovery and ... more details
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.
Genentech seeks a highly motivated Senior AI Scientist to join the Deep Learning Theory and Algorithms (DELTA) lab within the BRAID (Biology Research | AI Development) department in Genentech Research and Early Development (gRED). Our lab is dedicated to advancing deep learning research to support drug discovery and target discovery efforts, with a focus on large-scale foundation models in research biology. We are committed to driving innovation through cutting-edge ML methods with real-world impact in the drug discovery field.
The Opportunity
You will lead and execute a research program that develops novel foundational deep-learning methods with the ultimate aim of contributing to the drug discovery process. You will work in an exciting and multidisciplinary environment alongside AI scientists, ML engineers, computational biologists, and biological chemists, working on different areas of biology and chemistry.
You will lead the research, design, and execution of novel, cutting-edge ML research with applications to drug discovery and target discovery.
You will drive novel research on foundational AI methods for scientific problems, with a specific focus on foundation models, large-scale representation learning, and generative methods.
You will scale ML models to large biological datasets, working at the intersection of deep learning and engineering challenges to support new scientific questions.
You will regularly publish in top-tier ML venues (e.g., NeurIPS, ICLR, ICML, AISTATS, etc) and scientific journals, presenting results at internal and external scientific venues, conferences, and workshops.
You will collaborate closely with interdisciplinary and cross-functional teams across gRED.
Who you are
You have a PhD or equivalent experience in machine learning (?e.g.? Computer Science, Statistics, Computer Engineering, Applied Math, Physics, or related technical field).
You have excellent knowledge of the theory and practice of deep learning, as demonstrated in past projects and/or publications.
You have a strong publication record at top-tier ML venues such as NeurIPS, ICML, ICLR, AISTATS, etc.
You have excellent Python and PyTorch programming skills, with extensive knowledge of the best practices of software engineering, data engineering, and MLOps.
You have strong communication and collaboration skills.
Preferred
You have experience in developing and applying deep representation learning methods (e.g., generative, contrastive, graph-based, etc.), especially across different data modalities and at scale.
Prior experience in biology/chemistry is not required, but interest in biochemical drug discovery is valued.
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Relocation benefits are available for this posting
The expected salary range for this position based on the primary location of California is $165,200 - 306,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.