The Position
2026 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.
Translational Safety's Vision: We revolutionize and accelerate drug development through cutting-edge predictive, translational sciences.
Translational Safety's Mission: We develop transformative therapeutics for patients by delivering rigorous translational strategies and model-informed predictive insights for all therapeutic modalities through the entire drug development lifecycle via innovative experimental and computational approaches.
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 a few 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 talented summer intern who could expand the current capabilities of an internally developed LLM-powered AI agent which involves development a critical new module that enables the agent to access and query the non-clinical in vivo toxicity data in SEND format using natural language which includes not only raw data but also text-mined test-article related findings from study reports.
The primary technical objective is to implement a robust Text-to-SQL framework where the LLM, equipped with full awareness of the database schema, translates natural language questions into executable SQL queries.
Beyond standard retrieval, the agent must possess chemical intelligence to interpret and execute complex tasks including but not limited to chemical similarity and substructure searches.
The overarching goal is to democratize access to our extensive non-clinical data, specifically benefiting toxicologists, pathologists, and predictive toxicology scientists. By recognizing patterns within the wealth of in vivo data, the tool will assist users in comprehending compound toxicity across disparate studies, aggregating data for predictive modeling, and identifying test articles sharing specific substructures linked to specific adverse events.
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)
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: Computer Science, Cheminformatics, Bioinformatics, Computational Toxicology, Machine Learning & AI, Data Science or related fields.
Required Skills:
Advanced Python: Strong proficiency in Python for data manipulation and backend development.
Database Expertise: Deep understanding of relational databases and the ability to write and optimize complex SQL queries.
Generative AI & NLP: Experience working with Large Language Models (LLMs) and prompt engineering to interface with structured data.
Other: Good communication, collaboration, and interpersonal skills.
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.
Chemoinformatics: Familiarity with chemistry toolkits like RDKit and concepts such as molecular fingerprinting or substructure searching.
Scientific Data Standards: Knowledge of the SEND format or experience handling clinical/non-clinical study data.
Problem Solving: A desire to work at the intersection of life sciences and AI to solve real-world drug safety challenges.
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 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 (https://docs.google.com/forms/d/e/1FAIpQLSdZWlsbfQOvFVIQgHE_iDzWUTlhZvj6FytIzjS7xq6IGh1H5g/viewform) .
