2026 Summer Intern - Foundation Models, Prescient Design

Roche•South San Francisco, CA

4d•$50•Hybrid

About The Position

The Foundation Models team within Prescient Design (AI4DD) seeks exceptional graduate student interns with a demonstrated research background in deep learning applied to biological systems. We are looking for candidates with a passion for independent research and technical problem-solving to help us develop core Foundation Models that transform the drug discovery process. You will join a multidisciplinary team building and leveraging LLMs and biological Foundation Models for protein design and retrieval. This internship position is located in South San Francisco, on-site / NYC, on-site / Fully remote. The Opportunity Participate in cutting-edge research advancing Foundation Models for applications to drug discovery and protein design Develop well-documented code to facilitate adoption of the methods. Prepare a manuscript for submission to a scientific journal and/or conference. Program Highlights Intensive 12-week, full-time (40 hours per week) paid internship. Program start dates are in May/June (Summer) 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.

Requirements

Must be pursuing a PhD (enrolled student).
Computer Science, Machine Learning, Computational Biology, Structural Biology, Biophysics, or related technical field.
Deep Learning for Biology: Strong background and research experience applying deep learning to biological data
Software Engineering & DL frameworks: Strong proficiency in Python w/ extensive experience in modern deep learning frameworks (e.g. PyTorch). Ability to write modular, testable, and efficient code for model training and evaluation
Collaboration: Excellent and proactive communication, collaboration, and interpersonal skill

Nice To Haves

Hands-on experience with Transformer-based architectures and modern LLM paradigms
Experience with foundation models for biology or chemistry, large language models (LLMs), or geometric deep learning applied to biological problems.
Protein Data: Prior experience working with protein structural or sequential data.
Large-Scale Training & Infrastructure: Familiarity with distributed training techniques, GPU profiling/optimization, or working in High-Performance Computing (HPC) environments (e.g., Slurm, Docker, Kubernetes)
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

Participate in cutting-edge research advancing Foundation Models for applications to drug discovery and protein design
Develop well-documented code to facilitate adoption of the methods.
Prepare a manuscript for submission to a scientific journal and/or conference.