2026 Summer Intern – Large Language Models (Prescient Design / AI for Drug Discovery)
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About this role
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.
The Opportunity
- 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.
Program Highlights
- A 12-week, full-time paid internship (40 hours per week).
- Program start dates in May or June 2026.
- Location-based stipend to support internship expenses.
- Ownership of impactful, high-visibility projects.
- Collaboration with leading experts in biotechnology and AI.
Who You Are
Required Education
Must be pursuing a PhD (enrolled student).
Required Majors
Computer Science, Data Science, Machine Learning, Statistics, or a related technical field.
Required Skills
- 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.
Preferred Knowledge, Skills, and Qualifications
- 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.
Relocation benefits are not available for this job posting.