2026 Summer Intern - Computational Sciences Center of Excellence - Computational Biology and Medicine

Roche•South San Francisco, CA

21h•$50•Onsite

About The Position

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. The Immunology and CVRM team within the CS-CoE Computational Biology and Medicine department is seeking a talented and highly motivated Summer Intern with a strong computational and analytical background in computational biology, deep experience with single cell RNA-seq data, spatial transcriptomics, and integrating multi-omics data using neural networks. This internship position is located in South San Francisco, On - Site. The Opportunity Lead the integration of multi-omic clinical trial data using advanced neural network methods such as transformers, GNNs, and VAEs for patient classification, patient stratification, or to understand therapeutic response. Apply multi-omic integration methods to single cell/nuclei, spatial transcriptomics, and clinical data. Compare integration methods against existing ML approaches including existing foundational models and identify novel multi-omic insights including patient classification and biological insights. Work closely with established computational scientists, biomarker development scientists, and key stakeholders across the drug development pipeline. Program Highlights Intensive 12-week, 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.

Requirements

Must be pursuing a PhD (enrolled student).
Computational Biology, Bioinformatics, Artificial Intelligence and Machine Learning, Computer Science, Immunology or related field.
Proficiency in Python and PyTorch.
Track record of developing or applying neural networks, GNNs, VAEs, and machine learning techniques to perform multi-omic integration of single cell, spatial transcriptomics, proteomics, bulk, human genetics, or clinical tabular data.
Conceptual understanding of neural network architecture and multi-omic integration of data.
Expert proficiency with the single cell and spatial transcriptomics ecosystem, including Scanpy and working with large-scale AnnData objects.
Experience working with Git for version control and utilizing High-Performance Computing (HPC) resources.

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.
Knowledge of autoimmune diseases, such as Inflammatory Bowel Disease is a plus.
Detail-oriented, goal-driven, and dedicated to delivering high-quality results.
Use of GPU-enabled workflows is a plus.

Responsibilities

Lead the integration of multi-omic clinical trial data using advanced neural network methods such as transformers, GNNs, and VAEs for patient classification, patient stratification, or to understand therapeutic response.
Apply multi-omic integration methods to single cell/nuclei, spatial transcriptomics, and clinical data.
Compare integration methods against existing ML approaches including existing foundational models and identify novel multi-omic insights including patient classification and biological insights.
Work closely with established computational scientists, biomarker development scientists, and key stakeholders across the drug development pipeline.