2026 Summer Intern - Computational Sciences Center of Excellence - AI-Agentic Bioinformatics Tools for Clinical Biomarker Omics Workflows

2026 Summer Intern - Computational Sciences Center of Excellence - Scaling AI-Agentic Bioinformatics tools  for Clinical Biomarker Omics Workflows

Department Summary

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. 

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.

This internship position is located in South San Francisco, On-Site.

The Opportunity

We are seeking a Master’s level (or higher) graduate student to explore the cutting edge of AI-Agentic Bioinformatics. In this role, you will focus on translating standard bioinformatics command-line utilities into "MCP-servers".  Standardized interfaces that allow AI Agents (via clients like Cursor or Claude) to orchestrate complex analysis workflows using natural language.

You will move beyond traditional pipeline execution to build an intelligent tooling framework that allows scientists to "chat with their data," turning manual tool execution into scalable, agentic processes.

Key Responsibilities:

• Evaluate and Extend Bioinformatics Agentic Frameworks: Assess and extend Model Context Protocol (MCP) based frameworks for production readiness, modularity, and scalability within a clinical biomarker omics data environment.

• Tool-to-Agent Translation: Develop domain-specific MCP servers that wrap standard omics tools (e.g., for RNA-seq preprocessing, proteomics quantification), effectively translating CLI inputs/outputs into AI-readable contexts.

• Workflow Integration: Integrate these agentic servers with existing End-to-End (E2E) bioinformatics workflows/pipelines to ensure seamless execution in production.

• Benchmarking AI Performance: Benchmark the performance, reliability, and code-generation quality of Large Language Models (LLMs) when orchestrating these bioinformatics tasks.

• Strategic Recommendations: Propose recommendations for standardization, deployment, and scalability of agentic tools to democratize data analysis for scientific stakeholders.

Program Highlights

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

• Program start dates are on June 2nd (Summer) 2026.

• A stipend 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.

• Deliver final presentations of project work to senior leaders

• Professional & personal development curriculum throughout the program, including networking opportunities, workshops, and panel discussions

Who You Are

Required Education

Must be pursuing a Master’s degree or higher (enrolled student).

Required Majors

Bioinformatics, Computational Biology, Computer Sciences, Artificial Intelligence, Data Engineering, Data Sciences, Machine Learning, Biomedical Engineering, or a related field. 

Required S