Senior Machine Learning Scientist, AI for Drug Discovery (Frontier Research)

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

The Frontier Research team within Prescient Design is focused on advancing fundamental machine learning and its application to real-world challenges in drug discovery. Our mission is to uncover ideas and technologies that will make a meaningful impact on healthcare, shaping the future of how treatments are developed. Instead of focusing on incremental improvements, we tackle complex problems that require creative thinking and a broad perspective, working at a level that enables solutions to be applied across multiple areas. 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 recent advances in Bayesian methods offer the most promising paths for connecting these fields and building robust, impactful solutions.

In this role, you will:

• You will develop novel statistical methods that combine the strenghts of machine learning and Bayesian statistics

• You are expected to contribute to and drive publications, and present your results at internal and external scientific conferences.

• You will lead, collaborate, and execute on research that pushes forward the state of the art in machine learning for drug discovery.

• You will directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.

• You will work with a large and globally distributed team.

• You will help identify long-term ambitious research goals as well as intermediate milestones.

• You will prioritize research that can be applied to product development.

• You will mentor other team members. Play a significant role in healthy cross-functional collaboration.

Who You Are

• You have a PhD degree in Computer Science, Statistics, Machine Learning, Physics or related disciplines, or an MS degree in the above disciplines with 5+ years of industry research experience.

• You have demonstrated experience with Python and deep learning libraries such as Pytorch.

• You have demonstrated research experience, including at least one First author publications experience at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, AISTATS, and ACL).

• You have strong communication and collaboration skills.

Preferred:

• Expertise in the theoretical and/or computational aspects of Bayesian statistics

• Experience working with biomedical data

• Experience with research related to at least one of the following: variational inference, neural simulation-based inference, or large language models

• Public portfolio of computational projects (available on e.g. GitHub)

This opportunity needs to be based in South San Francisco, New York City, or Basel

Relocation benefits are available for this job posting

The expected salary range for this position based on the primary location of California, is $167.400 - 310,800. 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

#ComputationCoE

#tech4lifeComputationalScience

#tech4lifeAI

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