2026 Summer Intern - Research Pathology, Digital and Spatial Pathology (DSP)

Genentech • South San Francisco, CA • 4h ago

The Position

Department Summary

The Department of Pathology is embedded within Genentech's Research and Early Development Organization (gRED) and works to ensure that strategies for the treatment and cure of disease are based on accurate analyses of pathogenetic mechanisms. The department is a key driver in Genentech's Digital and Spatial Pathology efforts, developing cutting-edge tissue technologies to support scientific discovery. This internship is within the Digital Pathology Image Analysis - Spatial Omics (DPIA-SO) team, which specializes in collaborative computational analysis to provide scientists with actionable insights from high-dimensional imaging data.

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

The Opportunity

The intern will investigate next-generation computational methods aimed at optimizing the acquisition and analysis of highly multiplexed immunofluorescence images of tumor tissues (i.e Lunaphore COMET, CODEX Phenocycler). The project focuses on improving operational efficiency and image quality through advanced algorithmic approaches. Key responsibilities include:

Developing and evaluating computational frameworks to benchmark novel image processing and multiplex signal unmixing techniques.

Exploring the use of deep learning to resolve complex biological signals from multiplexed assays.

Investigating methods to computationally enhance image quality by reducing background noise and tissue artifacts.

Collaborating with upstream bench scientists to identify optimal experimental and computational strategies for high-dimensional data.

Providing regular updates and technical reports to project stakeholders.

Program Highlights

Intensive 12-week, 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.

Who You Are (Required)

Required Education

You meet one of the following criteria:

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

Must be pursuing a PhD (enrolled student).

Required Majors: Computational Biology, Bioinformatics, Mathematics, Statistics, Physics, Engineering, or other related quantitative/scientific fields.

Required Skills:

Experience with training, validating, and refining image-based deep learning models.

Proficiency in Python programming.

Strong problem-solving skills and critical thinking abilities.

Effective communication skills and the ability to work collaboratively in a team environment.

Preferred Knowledge, Skills, and Qualifications

Familiarity with digital pathology, bioimaging, or common deep learning frameworks such as PyTorch or TensorFlow.

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

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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