ML Research Intern (Summer 2026) – PhD (Computational Biology)

Where AI Meets Biomedicine—Innovate This Summer

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.

What You Will Do (Key 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

What you’ll bring

• 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

We’d be thrilled if you have experience with:

• 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)

Excited to apply?

To apply, please submit a cover letter detailing your interest in the role and relevant experience, along with your academic transcript and contact information for two academic references. We’re looking for passionate researchers eager to push the boundaries of AI in drug discovery—if that sounds like you, we encourage you to apply and join us in shaping the future of computational biology!

About Genesis Molecular AI

Genesis Molecular AI http://genesis.ml is pioneering foundation models for molecular AI to unlock a new era of drug design and development. The company’s generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis has raised over $300 million from leading AI, tech and life science-focused investors, signed multiple AI-focused research collaborations with major pharma partners, and is deploying GEMS to advance an internal therapeutics pipeline for a variety of high-impact targets.

Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.