2026 Summer Intern - Synthetic Molecule Process Chemistry

Genentech•Daly City, CA

8d•$45 - $50•Onsite

About The Position

Synthetic Molecule Process Chemistry at Genentech translates innovative drug candidates from discovery into safe, robust, and scalable Active Pharmaceutical Ingredient (API) manufacturing processes that enable clinical development. Scientists lead from the bench within cross-functional teams, developing enabling chemistry, selecting synthetic routes, and optimizing chemical processes through early-phase GMP manufacture. The role blends scientific creativity with practical execution across process design, safety, sustainability, and supply-chain strategy, in close partnership with internal CMC functions and external collaborators to advance Genentech’s pipeline. Speed is essential in early-stage process development; accordingly, the department leverages computational chemistry, machine learning, and robotics to accelerate the development cycle. This internship position is located in South San Francisco, on-site. The Opportunity The successful candidate will use cheminformatics tools to parameterize a set of ionizable lipids for lipid nanoparticles (LNP). They will use these parameters to develop machine learning models to predict the encapsulation efficiency, immune response, and in vivo distribution of LNPs. Using these models they will generate recommendations for new ionizable lipids and other components to refine the LNP delivery system. In collaboration with Synthetic Molecule Pharmaceutics (Formulations / Solid State), they will engage in multiple rounds of optimization, demonstrating lab in the loop optimization for LNPs. Using python libraries, the intern will develop visualizations to communicate their results. 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 or have attained an Associate's Degree.
Must be pursuing a Bachelor's Degree (enrolled student).
Must be pursuing a Master's Degree (enrolled student).
Must be pursuing a PhD (enrolled student).
Required Majors: Chemistry, Biology, Computer Science.
Candidate must have a background in chemistry or biology, including coursework in organic chemistry.
Candidate must have familiarity with python programming, preferably should be familiar with the standard scientific python stack (numpy, scipy, scikit-learn) including hands-on experience with machine learning methods.

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.
Familiarity with cheminformatics tools such as RDKit or OpenBabel.
Experience with building interactive visualizations in python.
Familiarity with Jupyter Notebooks.

Responsibilities

Use cheminformatics tools to parameterize a set of ionizable lipids for lipid nanoparticles (LNP).
Use these parameters to develop machine learning models to predict the encapsulation efficiency, immune response, and in vivo distribution of LNPs.
Using these models they will generate recommendations for new ionizable lipids and other components to refine the LNP delivery system.
In collaboration with Synthetic Molecule Pharmaceutics (Formulations / Solid State), they will engage in multiple rounds of optimization, demonstrating lab in the loop optimization for LNPs.
Using python libraries, the intern will develop visualizations to communicate their results.