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

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.

Relocation benefits are NOT available for this opportunity

This position must be based in South San Francisco, New York City, or Basel

The expected salary range for this position in California is $201,300 - 373,800. For New York City, the expected salary range is $192,500 - 357,500. 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

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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