2027 Summer Intern - Translational Safety (Computational Toxicology)

2027 Summer Intern - Translational Safety (Computational Toxicology) Department Summary Development Sciences (DevSci) spans the entire drug discovery and development cycle — from early stage research to drug commercialization.

Part of the drug development pipeline in DevSci includes the preclinical safety evaluation of candidate therapeutic molecules by toxicologists and pathologists in the Translational Safety (TS) department in order to enable further evaluation in humans.

Translational Safety is an integral part of DevSci. We contribute to the organization’s success by providing scientific insights and ensuring the safety of molecules that advance through the pipeline to patients.

We do this to support the DevSci vision to deliver the right drug in the right dose to the right patient. We are also committed to providing better outcomes for our people, patients, business, and communities by advancing and boldly championing diversity, equity, and inclusion in our work.

The Translational Safety organization is composed of several integrated sub-functions. This summer intern project falls within the Computational Toxicology sub-function.

The Computational Toxicology group enables early and accurate compound safety profiling by leveraging all relevant data (in vitro, ex vivo, in vivo), advanced analytics and computational modeling while closely working with other Translational Safety subfunctions such as Investigative Toxicology and Complex In Vitro Systems and subfunctions within gRED Computational Sciences Center of Excellence (CS CoE) organization.

This internship position is located in South San Francisco, on-site. The Opportunity The Computational Toxicology group is seeking a motivated summer intern to develop and apply computational approaches to biological questions in target safety research.

The project will use single-cell transcriptomic and multi-omics data, alongside AI/ML methods, to investigate biological systems relevant to preclinical safety assessment.

Working with an interdisciplinary team, the intern will have opportunities to: • 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.

The specific research focus may be refined based on the intern's interests and project needs. 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) Required Education You meet one of the following criteria: • Must be pursuing a Master's Degree (enrolled student).

• Must have attained a Master's Degree. • Must be pursuing a PhD (enrolled student). Required Majors: Bioinformatics, Computational Biology, Bioengineering, Computer Science, Data Science, Statistics, Machine Learning & AI, or related fields.

Required Skills • Coding: Experience with Python or R for scientific data analysis and computational workflows. • Bioinformatics: Familiarity with biological datasets (e.

g., gene expression or other omics data) and associated sample and experimental metadata. • Data Analysis: Ability to explore, quality-check, analyze, and interpret complex biological datasets.

• Research Practices: Ability to document methods and maintain reproducible analyses using version control tools. • Collaboration: Strong problem-solving, communication, and teamwork skills.

Preferred Knowledge, Skills, and Qualifications • 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. • Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Relocation benefits are not available for this job posting. The expected salary range for this position based on the primary location of  California is $41.

00-$52.00 hourly. 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 .