2025 Summer Intern - BRAID, Machine Learning for Trial Design

2025 Summer Intern - BRAID, Machine Learning for Trial Design.

Department Summary

We are seeking a highly skilled and motivated research intern to join the BRAID team (Biology Research | AI Development) within our Computational Sciences organization. At BRAID, we develop machine-learning methods for different applications in drug development. Within the clinical team in BRAID, we focus on building machine-learning methods to improve the design of early clinical trials. Fundamentally, we collaborate with clinical scientists, biologists, and engineers to design the next-generation clinical trials. 

This internship position is located in South San Francisco, on-site.

The opportunity 

• The ideal candidate should have hands-on experience in developing vision foundation mode to quantify imaging biomarkers.

• Implement and/or apply novel vision foundation model for clinical trials.

• Hands on experience with real clinical trial data.

• Train large-scale models using cloud and HPC infrastructure.

• Deliver high-quality code and actively participate in code reviews.

• Keep accurate and timely records of research findings and progress.

• If suitable, submit findings to a relevant conference or journal.

Program Highlights

• Intensive 12-week, full-time (40 hours per week) paid internship.

• Program start dates are in May and June 2024.

• 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.

• Final presentations of project work to senior leaders.

• Lead or participate in intern committees to design and coordinate program events and initiatives.

• Professional & personal development curriculum throughout the program, including networking opportunities, workshops, and panel discussions.

• Participate in volunteer projects, social events, and team-building activities.

Who you are (required)

Required Education:

• Must be pursuing a Ph.D. (enrolled student).degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Bioengineering, or a related field. 

• Deep understanding of vision foundation models and in-depth knowledge of current SoTA in medical foundation models, with ongoing research contributions in the field.

• Strong programming skills with practical experience working with large datasets.

• Proficient in Python and experience with state-of-the-art machine learning libraries (PyTorch, JAX).

• Familiarity with fundamental ML concepts and modern ML architectures (e.g., transformers).

Required Majors:

• Computer Science, Machine Learning, Statistics, Mathematics, Physics, Bioengineering, or a related quantitative field.

Preferred knowledge and skills 

• Strong publication record in conferences such as NeurIPS, ICML, ICLR, MICCAI or top general journals (Nature Medicine, Nature BME, JAMA, etc) or top  journals in a particular therapeutic area (Ophthalmology, JAMA Ophthalmology).

• Previous experience with large clinical or biological datasets.

• Experience with clinical data, imaging data, and multimodal data is highly desirable.

• ​Excellent communication, collaboration, and interpersonal skills.

• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Relocation benefits are not available for this job posting. 

The expected salary for this position based on the primary location of  California is $50 hour.  Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.

#GNE-R&D-Interns-2025

Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.