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1G
1000 Genentech, Inc.
Principal Machine Learning Scientist, Frontier Research, AI for Drug Discovery (AIDD)
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
Job Description
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. The Opportunity 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. In this role, you will:
- Develop theoretical frameworks and algorithms for sampling and generative modeling.
- Contribute to publications and present your results at internal and external scientific conferences.
- Collaborate and execute on research that pushes forward the state of the art in machine learning.
- Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.
- Work with a large and globally distributed team. Who You Are
- You have a PhD in Mathematics, Physics, Computer Science, Statistics, Machine Learning, or related disciplines with 2-7 years of relevant work experience.
- You have a strong publication record in generative modeling, with publications in academic journals like JMLR and at peer-reviewed ML conferences (e.g., NeurIPS, ICML, ICLR, and COLT).
- You have strong communication and collaboration skills.
Preferred:
- Published work with theoretical contributions.