ML Research Intern (Summer 2026) – PhD (Computational Biology)
About The Position
Join our Computational Biology team to help design and build an open, extensible research infrastructure that uses agentic AI to unify diverse biomedical data sources transforming AI into real-world biomedical breakthroughs. Our successful candidate will embark on a dynamic 12-week summer internship working at the intersection of agentic AI, scientific tool orchestration, and biomedical knowledge engineering. You'll build tool interfaces to major public data resources, develop methods for intelligent tool discovery and selection, and create validated analytical pipelines that AI agents can compose and execute. The internship culminates in a final presentation and report, giving you the opportunity to showcase your work and shape the future of how computational teams operate in drug discovery.
Requirements
- Currently enrolled in a graduate program in Computational Biology, Bioinformatics, Computer Science, or related field
- A strong Python programmer
- Familiarity with LLMs and an interest in agentic AI patterns (tool-use, function calling, multi-step reasoning)
- Experience working with APIs (REST or GraphQL) and building structured data pipelines
- Enough biological context to understand the scientific underpinnings of drug target identification and translational biology
- Strong analytical and problem-solving skills
Nice To Haves
- Scientific tool orchestration frameworks or custom agentic pipelines
- MCP (Model Context Protocol) or similar tool-serving frameworks
- Graph databases and knowledge graph construction
- Graph neural networks, knowledge graph embeddings, or graph reasoning techniques
- Working with large-scale public genomics platforms (DepMap, TCGA, CCLE, Open Targets, GWAS Catalog, etc)
- Open-source software development practices (version control, testing, documentation)
Responsibilities
- Help build infrastructure that connects AI agents to public biomedical databases (e.g., Open Targets, UniProt, DepMap, PubChem, GWAS Catalog, etc) and enables intelligent tool selection and use
- Design and prototype agentic workflows that chain data retrieval, analysis, and reasoning across heterogeneous sources to answer real drug discovery questions
- Explore approaches for tool discovery, workflow composition, and validation that make the platform increasingly useful over time
- Document architecture decisions and methodology; present your work to the research team
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What This Job Offers
Career Level
Intern
Education Level
No Education Listed
