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2026 Summer Intern - Clinical Imaging and Research & Early Clinical Development

Genentech
United States, California, South San Francisco
Jan 26, 2026
The Position

2026 Summer Intern - Clinical Imaging and Research & Early Clinical Development

Department Summary

The Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group dedicated to leveraging the transformative power of data, technology, and computational approaches. Our goal is to support scientists across both pRED and gRED in delivering more innovative and transformative medicines to patients globally.

The intern will join the imaging data insights team within the CS CoE. This role offers the chance to collaborate with a cross-functional team to develop and explore Artificial Intelligence (AI) models. Specifically, these models will utilize multiple data modalities for disease staging in patients with Alzheimer's disease.

This internship position is located in South San Francisco, On - Site.

The Opportunity

  • Advance machine learning models focused on staging tau burden in Alzheimer's Disease (AD), integrating anatomical MRI data with demographic information.

  • Perform comprehensive literature reviews on cutting-edge AI models for AD analysis and staging using imaging data.

  • Document all analytical findings and work closely with imaging and Machine Learning (ML) scientists to interpret results and evaluate their clinical relevance.

  • Prepare and deliver internal presentations; as appropriate, contribute to the development of abstracts or manuscripts for scientific publication.

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.

Who You Are (Required)

Required Education:

You meet one of the following criteria:

  • Must have attained a Master's Degree.

  • Must be pursuing a PhD (enrolled student).

  • Must have attained a PhD.

Required Majors: Mathematics, Computer Science, Statistics, Biomedical Engineering or relevant field.

Required Skills:

  • Good knowledge of programming in Python , frameworks such as PyTorch, computer vision techniques, CNN models, vision transformers, self-supervised models and hands-on experience in developing ML/DL models.

  • Strong knowledge of multi-modal AI model methodologies and familiarity with vision language foundation models for clinical applications.

  • Experience working with medical image processing (eg: freesurfer, SPM) and analysis of 3D images such as MRI, PET.

Preferred Knowledge, Skills, and Qualifications

  • Excellent communication, collaboration, and interpersonal skills.

  • Impeccable ethics, outstanding attention to detail, excellent project management, teamwork, and interpersonal skills, outstanding verbal and written communication.

  • Experience working in complex multi-disciplinary settings.

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.

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