2026 Summer Intern - Product Development Data Science & Analytics Digital Endpoints and Patients Centered Solutions, Medical Imaging (Oncology focus)

Genentech • South San Francisco, CA • 14h ago

The Position

Department Summary

We leverage complex datasets and external technologies, enabling advanced imaging applications that generate actionable insights at scale. Our mission is to unlock the full potential of data, to accelerate innovative healthcare solutions for patients and society. We aim to accelerate R&D and improve patient outcomes by developing innovative tools that enable faster, more accurate detection and monitoring of complex diseases.

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

The Opportunity

AI research: Suggesting and implementing new methodologies in machine learning and deep learning for algorithm development to enable scientific reverse translation.

Deep learning algorithm development: Develop, refactor, and evaluate deep learning models in an HPC/cloud computing environment with multi-modal data including clinical data and medical images.

Conduct rigorous validation of algorithms using standard metrics to ensure robust performance on clinical data.

Explore efficiencies in data flow from raw to analysis-ready datasets.

Program Highlights

Intensive 12-weeks, full-time (40 hours per week) paid internship.

Program start dates are in May/June 2026.

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.

Professional & personal development curriculum throughout the program, including networking opportunities, workshops, and panel discussions.

Who You Are (Required)

Required Education:

You meet one of the following criteria:

Must be pursuing a Master's Degree (enrolled student).

Must have attained a Master's Degree.

Must be pursuing a PhD (enrolled student).

Required majors: Computer Science, Biomedical Engineering, Data Science, Biotechnology or related fields.

Required skills:

Experience with generative models, foundation models applied to images/videos, stochastic systems, and/or Reinforcement Learning.

Strong engineering and statistical skills, particularly in designing and optimizing large-scale machine learning systems (e.g., PyTorch, Tensorflow).

Stay goal-oriented instead of method-oriented.

Preferred Qualifications

Excellent communication, collaboration, and interpersonal skills.

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

Passion for learning and curious about drug development in the pharmaceutical biotechnology industry.

Experience with software development workflow including versioning and code collaboration.

Experienced problem solver capable of seeking help when needed.

Familiarity with clinical data and medical images is a plus.

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 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 (https://docs.google.com/forms/d/e/1FAIpQLSdZWlsbfQOvFVIQgHE_iDzWUTlhZvj6FytIzjS7xq6IGh1H5g/viewform) .

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