2026 Summer Intern - DDC - Applied AI Engineer - Agents & Evaluation
Department Summary
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new computational sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
Within the CoE organisation, the Data and Digital Catalyst organisation drives the modernisation of our computational and data ecosystems and integration of digital technologies across Research and Early Development to enable our stakeholders, power data-driven science and accelerate decision-making.
CS-COE DDC is seeking exceptional undergraduate student interns with a strong interest in applied AI engineering, large language model (LLM) systems, and rigorous evaluation at production scale. Ideal candidates are motivated builders who can take ideas from research papers and translate them into robust, maintainable, and scalable solutions.
This internship position is located in South San Francisco, CA (on-site).
The Opportunity
Design and implement scalable evaluation pipelines for LLM-based agents across diverse use cases, with an emphasis on repeatability, robustness, and real-world relevance.
Build systems that go beyond PoCs by addressing edge cases, failure modes, versioning, and performance constraints in agent-based LLM systems.
Work closely with internal stakeholders to integrate evaluation frameworks into applied AI systems used across the drug discovery and development pipeline.
Intensive 12-week, full-time (40 hours per week) paid internship
Program start dates in May/June (Summer)
A stipend, based on location, to help offset living expenses
Ownership of meaningful, business-critical applied AI projects
Opportunity to work with experienced AI engineers and ML practitioners in the biotechnology industry
Who You Are
Required Education
Must be pursuing a Bachelor’s degree (currently enrolled student)
Must have attained a Bachelor's Degree (not currently enrolled in a graduate program).
Required Majors
Computer Science, Software Engineering, Data Science, Statistics, or a related technical field
Required Skills
Proficiency in Python and experience writing clean, modular, and testable code.
Familiarity with building services or pipelines that integrate multiple components (e.g., models, data sources, APIs).
Familiarity with common industry agent frameworks (langgraph, llamaindex, etc)
Experience with version control systems like Git
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.
Some experience with javascript
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location of CA is $50.00 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.