2026 Summer Intern - Computational Sciences Center of Excellence

Roche•South San Francisco, CA

1d•$45 - $45

About The Position

2026 Summer Intern - Computational Sciences Center of Excellence 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. The Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group whose goal is to harness this transformative lipower 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. The Data and Digital Catalysts (DDC) department within the CS CoE is a diverse, curious and action-driven team at the intersection of computation, engineering and science with ambition to advance our technical excellence. The focus of the team is on partnering with the informatics and scientific communities to create a computational and data ecosystem that powers scientific discovery and accelerates decision making. We aim to modernize our ability to acquire, store, link, share, find and analyze data across the organization through scalable and integrated solutions that truly make every data point count. The Opportunity Design, prototype, and evaluate an AI agent solution to replace traditional user interfaces across therapeutic drug discovery workflows. This project aims to leverage cutting-edge AI to improve efficiency and user experience. The goal is to make it more intuitive and straightforward for our scientists to collaborate on the design-build-test-learn loop and thus find lead drug candidates faster. Research & Analysis: analyze existing request systems, review available AI agents, and synthesize user feedback to identify pain points in current workflows. Prototyping & Development: set up an extensible AI agent framework, develop API/database connectors to link the agent to several target applications (LIMS, Registration systems, etc), and build a robust natural language processing (NLP) pipeline. Evaluation & Recommendation: conduct interviews and guide user testing to enable scientists to complete their end-to-end workflows using an agent connected to existing systems. Gather qualitative feedback on the prototype's usability. Program Highlights Intensive 12-weeks, full-time (40 hours per week) paid internship. Program start dates are in May 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

Required Education: Must be pursuing a Bachelor's degree (enrolled student).
Required major: Computer Science, Bioengineering, or related field.
Required Skills: Proficiency in at least one modern programming language (e.g., Python) with significant prior experience in developing robust software/scripting solutions.
Foundational knowledge and hands-on experience with AI: including agent frameworks, Large Language Models (LLMs), or Natural Language Processing (NLP) toolkits.
Experience retrieving data from informatics platforms and datastores: demonstrated proficiency in retrieving and integrating data using various methods, including MCP, REST, and querying databases with SQL.
Strong analytical problem-solving skills and a growth mindset: demonstrated by tackling complex, ambiguous technical challenges.
A keen interest in applying computational methods to drug discovery with enthusiasm for understanding complex scientific workflows.

Nice To Haves

Ability to execute projects and communicate effectively.
Excellent collaboration and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

Responsibilities

Research & Analysis: analyze existing request systems, review available AI agents, and synthesize user feedback to identify pain points in current workflows.
Prototyping & Development: set up an extensible AI agent framework, develop API/database connectors to link the agent to several target applications (LIMS, Registration systems, etc), and build a robust natural language processing (NLP) pipeline.
Evaluation & Recommendation: conduct interviews and guide user testing to enable scientists to complete their end-to-end workflows using an agent connected to existing systems. Gather qualitative feedback on the prototype's usability.