Heading

South San Francisco, CA

Internship

Onsite

$41/hr - $52/hr

Intern

Genentech is seeking a summer intern for its Computational Toxicology group within Translational Safety. The intern will develop computational approaches to biological questions in target safety research using single-cell transcriptomic and multi-omics data, alongside AI/ML methods, to support preclinical safety assessment.

Apply Now

Responsibilities

Develop reproducible computational workflows to integrate and analyze single-cell transcriptomic and multi-omics datasets
Characterize in vitro models and assess their biological relevance through quantitative analyses and comparisons with reference datasets
Explore and benchmark AI/ML methods, including foundation models, for analyzing high-dimensional biological data
Collaborate with computational and experimental scientists to interpret findings and contribute to reusable analysis tools, including LLM-based or agentic workflows where appropriate

Qualification

Required

Must be pursuing a Master's Degree (enrolled student)
Must have attained a Master's Degree
Must be pursuing a PhD (enrolled student)
Bioinformatics, Computational Biology, Bioengineering, Computer Science, Data Science, Statistics, Machine Learning & AI, or related fields
Experience with Python or R for scientific data analysis and computational workflows
Familiarity with biological datasets (e.g., gene expression or other omics data) and associated sample and experimental metadata
Ability to explore, quality-check, analyze, and interpret complex biological datasets
Ability to document methods and maintain reproducible analyses using version control tools
Strong problem-solving, communication, and teamwork skills

Preferred

Familiarity with single-cell RNA-seq concepts and analysis tools (e.g., Scanpy/AnnData, Seurat, or scVI)
Experience with differential expression, pathway analysis, or comparative transcriptomics across biological models
Interest in machine learning, representation learning, or foundation-model applications to biological data analysis
Familiarity with LLM-based tools and agentic workflows for scientific research