The Position

2026 Summer Intern - AI for Drug Discovery, Prescient Design

Department Summary

We seek a highly motivated research intern to join the Small-Molecule Artificial Intelligence for Drug Discovery Team at Prescient Design, within Genentech Research and Early Development (gRED). As a successful candidate, you will develop and apply novel machine learning methods, specifically Large Language Models (LLMs), to small-molecule drug discovery tasks, including molecular design. Our team fosters a collaborative approach that stimulates innovative thinking and the potential for creative breakthroughs at the forefront of ML research.

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

The Opportunity

• You will be applying LLMs to small molecules, with a focus on building models that can robustly reason about small-molecule chemistry

• Collaborate closely on machine learning projects for drug discovery with machine learning scientists, research engineers, computational biologists, chemists, and biologists.

• Develop and execute a research agenda focused on advancing chemical reasoning and generative AI for drug discovery.

• Report and present research findings and developments including status and results clearly, verbally and in writing (publishing in top-tier ML venues).

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.

• Deliver final presentations of project work to senior leaders

• Professional & personal development curriculum throughout the program, including networking opportunities, workshops, and panel discussions

Who You Are

Required Education: Must be pursuing a PhD (enrolled student).

Required Majors: Physical or Life sciences (e.g Chemistry, Biology, Physics), or Quantitative field (e.g. Computer Science, Statistics, Mathematics).

Required Skills:

• Strong research interest in Large Language Models, scientific reasoning, and chemical properties prediction.

• Excellent communication, collaboration, and interpersonal skills.

• Fluent in Python and experience with modern Python frameworks for deep learning (e.g. PyTorch or TensorFlow).

• Interest in LLMs for science and generative AI for molecular design.

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.

• Demonstrated computational research experience, as evidenced by publications, public code repositories, or equivalent.

• Experience with Natural Language Processing (NLP), Transformers, or Reinforcement Learning (RL).

• Experience with one or more cheminformatics or drug discovery toolkits (e.g. OpenEye, Schrodinger, RDKit, OpenBabel).

• Experience working with large chemical and biological datasets, including sequence, text, graph, and structure-based data.

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 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.