2026 Summer Intern - AI for Drug Discovery, Prescient Design

Genentech•New York City, NY

2d•$50 - $50•Onsite

About The Position

Prescient Design, within Genentech Research & Early Development (gRED), is a research group dedicated to the intersection of machine learning and drug discovery. Our mission is to leverage cutting-edge ML methods—particularly deep generative models and foundation models—to design novel molecules and understand complex biological systems. We apply state-of-the-art techniques to proteins, small molecules, and nucleic acids, conducting fundamental research to push the boundaries of what is computationally possible in healthcare. At Prescient Design, we’re building Lab‑in‑the‑Loop - Genentech’s platform that couples generative ML with automated wet‑lab experimentation to continuously design, test, and learn from new therapeutic molecules. The result is a closed feedback loop: models propose candidates, the lab runs assays, we ingest results, and the system gets smarter with every cycle. This isn’t theoretical: our Lab‑in‑the‑Loop work has already shown multi‑round optimization on clinically relevant targets, testing thousands of variants with large affinity gains. We are seeking exceptional graduate students with a strong research background in machine learning, as well as software design, and a passion for understanding the limits and capabilities of modern AI. You will join a team of ML scientists, engineers, and computational biologists to conduct fundamental research that pushes the boundaries of how we model molecules. This internship position is located in New York, NY, on-site.

Requirements

Must be pursuing a Master's Degree (enrolled student).
Must have attained a Master's Degree.
Must be pursuing a PhD (enrolled student).
Must have attained a PhD.
Required Majors: Computer Science, Mathematics, Computational Biology, Statistics.
Strong proficiency in Python with experience building modular, reusable codebases.
Strong experience designing, training, or evaluating deep learning models in modern machine learning frameworks (PyTorch) with a focus on reproducibility.
Rigorous adherence to modern software development best practices—including Git workflows, automated unit/integration testing, linting, and CI/CD—as well as proficiency with Docker and cloud infrastructure.
Familiarity with the challenges of multi-modal learning or representation learning.
Ability to read, critique, and implement methods from recent machine learning literature.

Nice To Haves

Familiarity with molecular data structures (proteins, small molecules).
Experience with the deployment of machine learning models.
Experience with benchmarking techniques for foundation models.
Proven publication record or experience contributing to the research community (e.g., NeurIPS, ICLR, ICML, or relevant domain journals).
Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

Participate in cutting-edge research in machine learning
Research and implement methods to evaluate the capabilities of large-scale foundation models across different data modalities
Analyze model performance on diverse biological tasks (e.g., binding prediction, property estimation) to identify failure modes and architectural improvements.
Integrate foundation models into our Lab-in-the-Loop product platform
Contribute to our internal codebases and open-source frameworks to facilitate reproducible research.
Collaborate with a cross-functional team of ML scientists, engineers, and computational biologists

Benefits

Intensive 12-weeks, 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.
paid holiday time off benefits

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