2026 Summer Intern - AIBT/TRAIL

Genentech, a member of the Roche group and founder of the biotechnology industry, is dedicated to pursuing groundbreaking science to discover and develop medicines for people with serious and life-threatening diseases. To solve the world's most complex health challenges, we ask bigger questions that challenge our industry and the boundaries of science to transform society. Our transformational discoveries include the first targeted antibody for cancer and the first medicine for primary progressive multiple sclerosis.

2026 Summer Intern

Department Summary
The Translational AI & Learning (TRAIL) group sits within Genentech’s AI for Biology & Translation (AIBT) department. AIBT develops and applies cutting-edge machine learning to decode complex biology, connect molecular data to human disease, and enable therapeutic impact. Our work spans modern foundation-model approaches, causal and predictive modeling, and real-world deployment in partnership with experimental and translational teams.
Interns in TRAIL work closely with scientists and engineers across computational biology, statistics, and ML engineering to deliver methods that are scientifically rigorous, scalable, and grounded in real biomedical problems.

Internship Overview
We are seeking a highly motivated graduate student intern to join TRAIL for Summer 2026. The intern will contribute to an active research project at the intersection of machine learning and translational biology, with opportunities to develop new modeling approaches, analyze high-dimensional biological data, and evaluate methods in realistic settings.
This internship is designed for candidates who enjoy owning a problem end-to-end—from formulating the question, to implementing models, to running careful evaluation, to communicating results clearly.

Projects may include :
• Predictive modeling of disease-relevant phenotypes from multi-modal biological data
• Benchmarking and evaluation frameworks for biological ML models
• Translational applications of foundation models for complex disease biology
• Building interpretable ML approaches to support target discovery and prioritization

This Internship is located in South San Francisco On Site.

The Opportunity
• Develop and implement machine learning models to address a defined translational biology problem using large-scale genomics datasets.
• Design rigorous benchmarking and evaluation experiments (metrics, baselines, ablations) to assess model performance and generalization.
• Curate, preprocess, and analyze high-dimensional biological data (e.g., single-cell and multi-modal datasets) to support modeling and interpretation.
• Collaborate closely with computational and experimental partners to translate scientific questions into actionable modeling approaches and deliverables.
• Communicate results clearly through well-documented code, concise summaries, and a final presentation to the broader team.

Program Highlights
• Intensive 12-week, full-time (40 hours/week) paid internship
• Program start dates are in May or 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.

Required Education
• Must be pursuing a PhD (enrolled student)
Required Majors
Computer Science, Computational Biology, Bioinformatics, Statistics, Data Science, Electrical Engineering, Applied Mathematics, or related quantitative field

Required Skills
• Strong programming skills in Python (experience with ML/data analysis workflows).
• Prior research experience in ML/AI, computational biology, genomics, or related domain (academic or industry).
Preferred Knowledge, Skills, and Qualifications
• Experience with deep learning frameworks (PyTorch preferred; TensorFlow ok)
• Familiarity with genomics / sequence modeling (e.g., DNA/RNA, regulatory genomics, variant effects)
• Experience working with large-scale datasets and reproducible workflows