2026 Fall Intern - Frontier Research, Prescient Design (AI for Drug Discovery)

Roche•South San Francisco, CA

2d•$40 - $50•Onsite

About The Position

The Frontier Research team within Prescient Design seeks exceptional graduate student interns with a demonstrated research background in machine learning or statistics, a passion for independent research and technical problem solving, and a proven ability to develop and implement ideas from research. Intern candidates should have a strong interest in decision theory and adaptive experimental design. This internship position is located in South San Francisco, on site. The Opportunity Participate in cutting-edge research in active simulation-based inference. Collaborate with cross-functional teams to identify problem settings shared by impactful applications. Develop well-documented code to facilitate adoption of the method. Present results in the form of a publication, for submission to internal and external scientific venues.

Requirements

Must be pursuing a PhD (enrolled student).
Majors: Computer Science, Statistics, Applied Math, Physics, or related technical field.
Strong publication record and experience contributing to research communities, including journals and conferences like NeurIPS, AISTATS, ICML, and ICLR.
Strong software development skills in Python.
Ability to reason formally about explore-exploit tradeoffs in adaptive inference procedures.
Research experience in frameworks involving sequential decision making under uncertainty.

Nice To Haves

General familiarity with simulation-based inference and causal inference is a plus.
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 active simulation-based inference.
Collaborate with cross-functional teams to identify problem settings shared by impactful applications.
Develop well-documented code to facilitate adoption of the method.
Present results in the form of a publication, for submission to internal and external scientific venues.