2026 Summer Intern - Computational Biology and Medicine - Perturbation

Genentech•Daly City, CA

2d•$50 - $50•Onsite

About The Position

The Position 2026 Summer Intern - Computational Biology and Medicine - Perturbation Department Summary The Computational Biology and Medicine (CBM) department is a key pillar of the Computational Sciences Center of Excellence (CS-CoE) within Genentech’s Research and Early Development (gRED) organization. We are a diverse team of computational biologists and data scientists with deep expertise spanning genomic methodologies, multi-modal data analysis, and therapeutic areas. In CBM, we bridge the gap between dry-lab and wet-lab. We collaborate closely with disease biologists, experimentalists, and AI/ML researchers to deploy rigorous analytical frameworks that drive decision-making. Our mission is to transform complex biological data into actionable insights that accelerate target discovery and biomarker development. This internship position is located in South San Francisco, on-site. The Opportunity As a computational biology intern, you will join a vibrant, rigorous, and industry-leading research community. You will have the chance to contribute directly to our early discovery pipeline by developing tools to interpret large-scale genetic screening data. You will: Explore and Analyze Perturbation Data: Dive into large-scale genetic perturbation datasets (e.g., Perturb-seq) to understand the landscape of gene function and regulation. Benchmark Machine Learning Methods: Rigorously test and evaluate statistical and machine learning approaches, such as matrix factorization and other dimensionality reduction techniques, to identify robust gene expression programs and perturbation modules. Innovate Annotation Strategies: Develop and apply computational methods to biologically annotate identified modules, connecting statistical signals to meaningful biological mechanisms. Build Scientific Software: Design and implement software tools to database, query, and visualize results, enabling wider access to these insights for the broader research team. Collaborate and Present: Engage with cross-functional teams and present your research at internal seminars. 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.

Requirements

Must be pursuing a PhD (enrolled student).
Computer Science, Statistics, Mathematics, Computational Biology, Biostatistics, or a related field.
Proficiency in bioinformatics (programming in R or Python, and HPC).
Experience developing/applying statistical or computational methods to analyze omics data.
Experience with scRNA-seq data
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Nice To Haves

Excellent communication, collaboration, and interpersonal skills.
Experience with dimensionality reduction techniques and/or perturbation data.

Responsibilities

Explore and Analyze Perturbation Data: Dive into large-scale genetic perturbation datasets (e.g., Perturb-seq) to understand the landscape of gene function and regulation.
Benchmark Machine Learning Methods: Rigorously test and evaluate statistical and machine learning approaches, such as matrix factorization and other dimensionality reduction techniques, to identify robust gene expression programs and perturbation modules.
Innovate Annotation Strategies: Develop and apply computational methods to biologically annotate identified modules, connecting statistical signals to meaningful biological mechanisms.
Build Scientific Software: Design and implement software tools to database, query, and visualize results, enabling wider access to these insights for the broader research team.
Collaborate and Present: Engage with cross-functional teams and present your research at internal seminars.