2025 Fall Intern - gCS BRAID, Generative Models for Multimodal Data

2025 Fall Intern - gCS BRAID, Generative Models for Multimodal Data

Department Summary

We're looking for a highly skilled and motivated Research Intern to join our BRAID (Biology Research | AI Development) team within the Computational Center of Excellence.

At BRAID, we develop cutting-edge machine learning methods for various applications in drug development and target discovery. As an intern on our clinical team, you'll specifically focus on building machine learning methods that can inform clinical trial design and advance biological understanding. This means you'll be collaborating closely with machine learning scientists, domain experts, and engineers to design the next generation of biological foundation models.

The ideal candidate will have hands-on experience developing Generative AI models for biological data. We're especially interested in candidates with experience in multimodal models and data fusion techniques, as well as prior work with RNA/DNA/Omics data.

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

The Opportunity

• Design and implement novel Generative AI models that push the boundaries of biological understanding.

• Train large-scale models leveraging cutting-edge cloud and High-Performance Computing (HPC) infrastructure.

• Deliver high-quality code and actively contribute to our collaborative code review process.

• Maintain accurate and timely records of your research findings and progress.

• Collaborate closely with a diverse team of experts across various scientific and engineering disciplines.

• Present your findings at relevant conferences or submit them to journals, if suitable.

Program Highlights

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

• Program start dates are in September (Fall).

• 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.

Who You Are

Required Education:

• Must be pursuing a PhD (enrolled student).

Required Majors: 

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

Required Skills: 

• Deep understanding of Generative AI Models, with active research involvement in this field. Experience in training/designing large-scale 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:

• Excellent communication, collaboration, and interpersonal skills.

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

• Strong publication record in conferences such as NeurIPS, ICML, ICLR.

• Previous experience with large clinical or biological datasets.

Relocation benefits are not available for this job posting. 

The expected salary range for this position based on the primary location of  California is $50 hourly.  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. 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.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.