2025 Summer Intern - Machine Learning for Trial Design

2025 Summer Intern - 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.

The successful candidate for this role should have practical experience working with knowledge graphs, large language models, and state-of-the-art machine-learning libraries. Ideal candidates would have a working knowledge of graph-based transformers and be familiar with clinical data or electronic health records (EHR).

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

The opportunity

• Design and implement novel graph-based methods applied to knowledge graphs and tailored to the complexities of 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.

• Ownership of challenging and impactful business-critical projects.

• A stipend, based on location, will be provided to help alleviate costs associated with the internship.

• 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. degree

Required Majors: Computer Science, Machine Learning, Statistics, Mathematics, Physics, Informatics, Bioinformatics, Health Informatics, or a related field.

Required Skills:

• Experience with LLMs and agent-based workflows

• Experience with large structured databases such as knowledge graphs

• Experience with training graph-based machine learning models

• 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)

Preferred Knowledge, Skills, and Qualifications:

• Strong publication record and experience contributing to research projects via publications in conferences such as NeurIPS, ACL, ICML, ICLR

• Previous experience with large clinical datasets (EHR, RWD)

• Previous experience with biology concepts

• 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.

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Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

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