2026 Summer Intern (Computational Sciences - Cluster of Excellence)

Roche•South San Francisco, CA

1d•$50 - $50•Onsite

About The Position

The Marioni group in the Computational Sciences (CS) department studies the molecular mechanisms of cell fate decisions in early development, aging, immunology and cancer. The group has been a driver of the single cell genomics revolution, pioneering the development of robust and widely-used statistical approaches for all aspects of data analysis, from normalization through to data integration and interpretation. We are seeking a motivated, rigorous and creative individual with a strong computational background for data analysis / method development geared towards large-scale genetic perturbation data. This internship position is located in South San Francisco, On - Site. The Opportunity Contribution to computational toolkits for mapping and extracting insights from single-cell transcriptomic and imaging data. Exposure to cutting-edge genetic screening technologies and data (Perturb-seq, Optical Pooled Screens). Close collaboration with computational and experimental partners to translate scientific questions into computational analyses and methods. Clear communication of results through well-documented code, concise summaries, and a final presentation to the broader team. 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 Education: You meet one of the following criteria: Must have attained a Master's Degree. Must be pursuing a PhD (enrolled student). Required Majors: Computational Biology, Bioinformatics, Data Science, Statistics, Computer Science, Biomedical Engineering, Systems Biology (or related quantitative field). Required Skills: Strong programming skills (experience with ML/data analysis workflows). Foundations in statistics and/or machine learning. Prior research experience in ML/AI, computational biology, genomics, or related domain (academic or industry). Ability to write clean, reproducible code (version control, documentation, notebooks/pipelines). Preferred Knowledge, Skills, and Qualifications Excellent communication, collaboration, and interpersonal skills. Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion. Prior experience with single cell genomics and/or imaging data. Prior experience with deep learning frameworks. 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. Who We Are 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. The next step is yours. To apply today, click on the "Apply for this job" button. 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.

Requirements

Must have attained a Master's Degree.
Must be pursuing a PhD (enrolled student).
Computational Biology, Bioinformatics, Data Science, Statistics, Computer Science, Biomedical Engineering, Systems Biology (or related quantitative field).
Strong programming skills (experience with ML/data analysis workflows).
Foundations in statistics and/or machine learning.
Prior research experience in ML/AI, computational biology, genomics, or related domain (academic or industry).
Ability to write clean, reproducible code (version control, documentation, notebooks/pipelines).

Nice To Haves

Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
Prior experience with single cell genomics and/or imaging data.
Prior experience with deep learning frameworks.

Responsibilities

Contribution to computational toolkits for mapping and extracting insights from single-cell transcriptomic and imaging data.
Exposure to cutting-edge genetic screening technologies and data (Perturb-seq, Optical Pooled Screens).
Close collaboration with computational and experimental partners to translate scientific questions into computational analyses and methods.
Clear communication of results through well-documented code, concise summaries, and a final presentation to the broader team.