2026 Summer Intern - Machine Learning for Small-Molecule Drug Discovery

Roche•South San Francisco, CA

23h•$50•Onsite

About The Position

2026 Summer Intern - Machine Learning for Small-Molecule Drug Discovery Department Summary We seek a highly motivated research intern to join the Small-Molecule Machine Learning for Drug Discovery Team at Prescient Design, within Genentech Research and Early Development (gRED). As a successful candidate, you will develop and apply novel machine learning methods to molecular design with the ultimate goal of advancing drug discovery. Our team fosters a collaborative approach that stimulates innovative thinking and the potential for creative breakthroughs at the forefront of ML research. This internship position is located in South San Francisco, on site. The Opportunity Develop and apply machine learning methods that learn from internal Genentech small molecule data. Collaborate closely on machine learning projects for drug discovery with machine learning scientists, research engineers, computational biologists, chemists, and biologists. Develop and execute a research agenda focused on machine learning for drug discovery. Design, implement, and evaluate machine learning models. Clearly report and present research developments and findings, both verbally and in writing (publishing in top-tier ML venues). Program Highlights Intensive 12-weeks, full-time (40 hours per week) paid internship. Program start dates are in May/June (Summer) A stipend, based on location, will be provided to help alleviate costs associated with the internship. Ownership of challenging and impactful business-critical projects. Work with some of the most talented people in the biotechnology industry.

Requirements

Must be pursuing a PhD (enrolled student).
Requires Majors: Computer science, chemistry, or a related field.
Basic chemistry & cheminformatics knowledge.
Machine learning expertise.
Fluent in Python and experience with modern Python deep learning frameworks.
Interest in drug discovery, chemical property prediction, and applying methods from machine learning academia to real-world problems.

Nice To Haves

Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
Strong research interest in drug optimization and discovery, chemical properties prediction.
Demonstrated computational research experience, as evidenced by publications, public code repositories, or equivalent.
Experience with one or more cheminformatics or drug discovery toolkits (e.g. OpenEye, Schrodinger, RDKit, OpenBabel).
Experience working with large chemical and biological datasets, including graph, sequence, and structure-based data.

Responsibilities

Develop and apply machine learning methods that learn from internal Genentech small molecule data.
Collaborate closely on machine learning projects for drug discovery with machine learning scientists, research engineers, computational biologists, chemists, and biologists.
Develop and execute a research agenda focused on machine learning for drug discovery.
Design, implement, and evaluate machine learning models.
Clearly report and present research developments and findings, both verbally and in writing (publishing in top-tier ML venues).

Benefits

Intensive 12-weeks, full-time (40 hours per week) paid internship.
Program start dates are in May/June (Summer)
A stipend, based on location, will be provided to help alleviate costs associated with the internship.
Ownership of challenging and impactful business-critical projects.
Work with some of the most talented people in the biotechnology industry.
paid holiday time off benefits

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