Machine Learning Scientist Intern

2026 Machine Learning Scientist Summer Intern - BRAID

This internship position is located in South San Francisco, on site.

Department Summary

At Genentech Research & Early Development (gRED) we have initiated an exciting journey to bring together and further strengthen our computational talent and capabilities by forming a new, central organization - gRED Computational Sciences Center of Excellence (CS CoE). CS CoE is on a mission to partner across the organization to realize the potential of data, technology and computational approaches that will revolutionize how targets and therapeutics are discovered and developed, ultimately enabling novel treatments for patients across the world. We stand at the beginning of this exciting journey.

BRAID (Biology Research | AI Development) is a department within CS CoE that focuses on developing and applying machine learning methods to impact biological discovery, with the ultimate goal of impacting drug discovery and human health.

We are searching for a motivated summer intern to work on designing, developing, and interpreting new generative models for biological data. The candidate will further our work on multi-scale generative models that can be used to advance programs in gene and cell therapy.

The internship position is located in South San Francisco, onsite.

The opportunity

• The intern will lead the development of a novel generative method for biological data.

• A preliminary step will require basic understanding of the data, its features and required processing steps.

• To benchmark the generic approach, publicly available datasets with existing supervision can be used for performance evaluation.

• There will be opportunities to apply the method on novel data collected in the lab, targeting dedicated biological questions.

• The intern will produce weekly reports of his progress and compile their findings in a final project report that could be used as a basis for a machine learning conference paper submission.

• The intern will continuously suggest new experiments and modeling strategies based on previous results.

• Opportunity to shape the research agenda of the project

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.

Who You Are

Required Education:

Must be pursuing a PhD (enrolled student).

Required Majors:

Computer Science, Electrical Engineering, Mathematics, Statistics or related field.

Required skills:

• Excellent communication, collaboration, and interpersonal skills.

• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

• Experience with large scale deep learning training infrastructure.

• Strong foundations in ML theory

• Familiarity with generative models (flow matching, diffusion models,...)

• Excellent academic presentation and writing skills

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of California is $50.00 per hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

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.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.