Translational Safety 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.

• 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, and clinical), advanced analytics, and computational modeling while closely collaborating with other Translational Safety groups such as Investigative Toxicology and Complex In Vitro Systems (CiS).

This internship position is located in South San Francisco, on-site.

The Opportunity

The Computational Toxicology group is seeking a talented and motivated summer intern with a strong foundation in toxicology to expand the predictive capabilities of our translational safety data platforms. A central challenge in preclinical drug safety is understanding how effectively early in vitro screening assays translate to human clinical safety outcomes, and identifying which toxicological liabilities our current screening suites fail to capture.

This project will focus primarily on evaluating how well internal in vitro bioactivity screening assays predict clinical adverse drug reactions (ADRs), using machine learning and statistical modeling frameworks built in R. As a secondary objective, the project will assess whether incorporating public in vitro bioactivity datasets can capture safety signals that internal assays may overlook.

The intern will directly drive:

• Data Curation & Harmonization: Ingesting, cleaning, and standardizing internal in vitro screening readouts alongside clinical safety and adverse event data, with targeted integration of relevant public bioactivity datasets as needed.
• Translational Benchmarking & Predictive Modeling: Applying statistical frameworks and machine learning techniques in R to correlate in vitro bioactivity profiles with observed clinical adverse events across target organ systems (e.g., drug-induced liver injury, cardiovascular findings).
• Assay Concordance & Gap Analysis: Evaluating the sensitivity and predictive limits of our internal screening battery against clinical outcomes, exploring whether complementary public assay data can fill specific translational blind spots.

Ultimately, these insights will directly inform the translational utility of our current screening battery, highlighting actionable avenues to guide future in-house in vitro assay development and build more robust machine learning models for early predictive toxicology.

Key Responsibilities

• Extract, clean, and harmonize clinical trial safety data and adverse event findings across internal and external clinical datasets for target chemical space.
• Aggregate, curate, and standardize high-throughput in vitro bioactivity screening data from internal discovery databases, incorporating relevant public bioactivity data where appropriate.
• Integrate curated in vitro and clinical safety datasets into internal translational safety data platforms using R.
• Develop, apply, and evaluate machine learning models and statistical benchmarking workflows in R to determine how reliably early assay readouts predict human clinical adverse events.
• Perform translational gap analyses to characterize which clinical toxicities are well-predicted by current screening batteries versus those that require new or complementary assay strategies.
• Synthesize findings and support data-driven recommendations on assay performance and future predictive toxicology strategies to computational toxicologists, discovery toxicologists, and cross-functional teams.

Program Highlights

• Intensive 12-week, full-time (40 hours per week) paid internship.
• Program timeframe: May/June 2027 through August/September 2027.
• Direct ownership of an impactful translational toxicology and predictive modeling project.
• Mentorship from senior computational toxicologists and cross-functional exposure to discovery toxicology and translational safety disciplines.
• A stipend, based on location, will be provided to help alleviate costs associated with the internship.

Who You Are

Required Education:

• Currently enrolled in a PhD program in Toxicology, Pharmacology, Computational Biology, or a related biomedical discipline.

Required Skills & Qualifications:

• Fluency in R: Strong proficiency in R programming (e.g., tidyverse, data wrangling, statistical analysis, and data visualization).
• Toxicology Background: Solid academic grounding in toxicology, pharmacology, or biomedical sciences, with a clear understanding of mechanisms of toxicity and adverse drug reactions.
• In Vitro / In Vivo Study Understanding: Working knowledge or research background in in vitro and/or in vivo toxicology study designs, assay readouts (e.g., IC50/AC50, target engagement, cytotoxicity), and toxicological endpoints.
• Machine Learning for Predictive Toxicology: Working understanding of machine learning techniques (e.g., classification, regression, clustering, model evaluation metrics such as ROC-AUC, sensitivity, and specificity) applied to toxicological or biological data.
• Communication & Presentation: Ability to communicate technical findings and toxicological interpretations clearly to cross-functional scientific teams.
• Mindset: Highly organized, detail-oriented, with high curiosity and eagerness to learn about preclinical safety evaluation and the drug development lifecycle.

Preferred Qualifications:

• Familiarity with public toxicology and chemical biology databases (e.g., ToxCast/Tox21, ChEMBL, PubChem).
• Experience reviewing or working with preclinical/clinical trial documentation (e.g., study reports or MedDRA adverse event coding).
• Experience evaluating assay concordance, predictive value, and translational relevance of early screening models to organ-specific toxicities.

Relocation benefits are not available for this job posting. 

The expected salary range for this position based on the primary location of California is $52.00 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.