2026 Summer Intern – Large Language Models (Prescient Design / AI for Drug Discovery)

Genentech•New York, NY

2d•Onsite

About The Position

2026 Summer Intern - Large Language Models (Prescient Design / AI for Drug Discovery) Department Summary Prescient Design, part of Genentech’s Research and Early Development (gRED) organization, advances drug discovery through cutting-edge machine learning. Our Foundation Models team builds internal large language models (LLMs) that enable next-generation scientific and biomedical applications across the drug-discovery pipeline. We are seeking exceptional graduate student interns with strong ML research or engineering backgrounds, the ability to drive independent exploration, and a record of solving complex technical problems in collaborative settings. This internship is on-site in New York City.

Requirements

Must be pursuing a PhD (enrolled student).
Computer Science, Data Science, Machine Learning, Statistics, or a related technical field.
Strong Python skills and experience with ML frameworks such as PyTorch.
Solid understanding of neural networks, representation learning, and modern supervised/unsupervised methods
Excellent written and verbal communication, and ability to work effectively with interdisciplinary teams.

Nice To Haves

Hands-on experience with large language models, especially post-training workflows (e.g., supervised fine-tuning and reinforcement learning) to improve instruction following, tool use, reasoning, and domain-specific performance.
Experience with GPU clusters or distributed training systems for efficient large-scale model training.
Exposure to drug discovery workflows, biomedical data analysis, or related life-science applications is a plus but not required.

Responsibilities

Contribute to research and development of internal LLMs for scientific discovery and therapeutic molecular design.
Develop and evaluate advanced post-training techniques to enhance domain knowledge and strengthen reasoning capabilities for scientific and biomedical applications.
Support large-scale model training on high-performance GPU clusters.
Collaborate with cross-functional teams to design and implement applied LLM use cases.