2026 Summer Intern - gCS AIDD

2026 Summer Intern - gCS AIDD

Department Summary

Prescient Design is seeking exceptional graduate interns with strong research experience in machine learning (ML) and a passion for applying advanced algorithms to scientific discovery. Our team develops state-of-the-art generative models and foundational ML methods to accelerate drug design. This internship focuses on Reinforcement Learning from Human Feedback (RLHF) for molecular generation, adapting techniques that revolutionized large language models to align molecular design models with chemist expertise. See our Google Scholar page for recent publications.

Intern candidates should have a strong interest in generative modeling, reinforcement learning, preference learning, and novel mechanisms for guided and controllable generation. The ideal intern is comfortable conducting independent research, rapidly prototyping ideas, and collaborating with multidisciplinary scientists.

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

Key Responsibilities

• Participate in cutting-edge research in ML, 3D generative models, and applications to drug discovery

• Collaborate with cross-functional teams to deliver an impactful, business-critical project

• 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 conferences

Program Highlights

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

• Program start dates are in May/June (Summer)

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

Who You Are (Required)

Required Education

• Current Ph.D. student in Computer Science, Engineering, Statistics, Applied Mathematics, Computational Biology, Computational Chemistry, Physics, or related technical field.

Required Skills

• Strong publication record or evidence of impactful research contributions (e.g., NeurIPS, ICML, ICLR, AISTATS, TMLR, CVPR, ICCV/ECCV)

• Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX)

• Experience in at least one of the following areas:

• Reinforcement learning or preference learning

• Generative modeling (e.g., diffusion models, autoregressive models)

• Graph neural networks or molecular representation learning

Preferred Skills

• Experience with RLHF methods (e.g., DPO, PPO-RLHF, reward modeling)

• Familiarity with molecular modeling, cheminformatics, or drug discovery applications

• Contributions to open-source ML frameworks or reproducible research environments

• Excellent communication, collaboration, and interdisciplinary working skills

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of California is $50.00 per 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.

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.