2026 Summer Intern - Regev Lab - Feedback Driven AI Systems

Roche•South San Francisco, CA

1d•Onsite

About The Position

Advances in AI, data, and computational methods are rapidly transforming how scientific questions are asked and answered. The Regev and Lubeck Labs are seeking a highly motivated graduate intern to contribute to research on interactive, feedback-driven AI systems that learn from multimodal computer vision and text data and can help streamline complex, real-world experimental workflows. This internship position is located in South San Francisco, on-site. The Opportunity This internship offers an opportunity to work closely with a cross-functional team of machine learning scientists and experimental biologists, and mentored by researchers from the Regev and Lubeck Labs. The internship will involve working at the intersection of real-world multimodal modeling, learning from feedback, and scalable ML systems in close partnership with experimental collaborators. The intern will own a well-scoped research project that balances methodological innovation with practical considerations, emphasizing rigorous problem formulation, careful evaluation, and reproducible prototypes that deliver clear scientific value. Program Highlights Intensive 12-weeks, full-time (40 hours per week) paid internship. Program start dates are in May/June 2026. 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 Master's Degree (enrolled student).
Must be pursuing a PhD (enrolled student).
Computer Science, Robotics, Machine Learning, or Computational Biology.
Multimodal machine learning: Experience developing or adapting models that learn from multiple data types (e.g., video/images and text). Video processing experience is highly desirable.
Learning from feedback: Familiarity with methods for improving model behavior using feedback signals and designing closed-loop training/evaluation workflows.
Data & ML engineering at scale: Ability to build reliable pipelines for large, heterogeneous datasets (e.g., video or time-series), including data ingestion, preprocessing, quality control, labeling/annotation workflows, and efficient training/inference infrastructure.
Research experience and skills: Experience in formulating research questions, designing experiments, and communicating technical results clearly.

Nice To Haves

Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.