2026 Summer Intern - Biology Research | AI Development (BRAID)

2026 Summer Intern - Biology Research | AI Development (BRAID)

Department Summary

Biology Research | AI Development (BRAID) department is a part of the Computational Sciences Center of Excellence in Genentech. Our mission is to push the boundaries of biological discovery by employing and developing cutting-edge AI/ML methodologies. The perturbation team at BRAID has a specific focus on target discovery from diverse data modalities generated within Genentech's Research Division. Our department offers a highly dynamic and cooperative environment at the intersection of computational methods and scientific research. A successful candidate for this role will work closely with machine learning scientists and computational biologists to develop and evaluate interpretable foundation models on perturb-seq data across cell lines.

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

The Opportunity

• Explore representation learning on perturb-seq data.

• Evaluate the quality of learned representations for biological discovery and their ability to reduce the effect of unwanted confounders.

• Develop methods for interpreting biological phenotypes.

• Develop algorithms for disease mechanism and pathway inference on perturbation maps.

• Present scientific findings to the BRAID Department and the Computational Sciences organization.

• Submit findings to relevant scientific conferences and/or journals.

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.

Who You Are

Required Education
Must be pursuing a PhD (enrolled student).

Required Majors

Quantitative/computational fields such as Computational Biology, Computer Science, Statistics, Mathematics, or similar.

Required qualifications:

• Strong foundation in machine learning.

• Practical experience with deep learning and common Python-based libraries.

• Familiarity with scRNA-seq data analysis.

• Excellent communication and collaboration skills.

Preferred qualifications:

• Familiarity with high-content perturbation data.

• Familiarity with machine learning topics such as representation learning, graph theory, interpretability, and feature disentanglement.

• Familiarity with scRNA-seq data, GWAS data, and human genetics.

• Track record of tackling challenging biological problems with advanced computational methods.

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.

• Strong programming skills in Python (e.g. numpy, pandas, scikit-learn)

• Experience with deep learning frameworks such as PyTorch or TensorFlow

• Solid understanding of machine learning and representation learning concepts.

• Familiarity with single-cell RNA-seq or Perturb-seq data.

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.