AI Engineer Internship | Undergraduate or Graduate - Summer 2027

About the role

Endeavor's Platform team builds the data, tools, and AI infrastructure that powers our global network across 50+ markets. With Claude Enterprise now deployed to 390+ staff across 43+ markets, access to AI is no longer our constraint. The next challenge is organizational: converting that access into institutional capabilities that one team can build, another market can reuse, and Endeavor retains when people move on. We're looking for a curious, hands-on, builder-minded intern to help create Endeavor's AI organizational infrastructure: the reusable capabilities, templates, governance rails, and enablement systems that let our local offices and functional teams design their own AI-augmented processes safely and effectively, instead of 50 markets reinventing the same tools in isolation. This is not a shadowing role. It is a hybrid role between discovery and applied AI engineering. You will interview local offices and functional departments to understand their workflows and what infrastructure they actually need, and you will get your hands dirty: prototyping agentic workflows, Claude skills, MicroApps, and retrieval systems on Endeavor's enterprise AI stack (Claude Enterprise, MCP integrations, Snowflake, Salesforce, and Notion). You will also support the Platform team's deeper work on knowledge capture and institutional memory, helping turn the network's interactions into structured, trusted, actionable knowledge. By the end of the internship, you'll have shipped real capabilities used by teams across markets, creating a tangible output with lasting impact on how nearly 500 Endeavor colleagues work worldwide. This internship is ideal for someone excited by applied AI, organizational systems, and building things that scale globally, while gaining real responsibility and visibility inside a mission-driven organization.

Responsibilities

• Discover What Teams Actually Need: Lead structured interviews with local offices and functional departments to map their workflows, pain points, and existing AI experiments. Find where a shared, governed capability would beat fifty isolated ones, and identify what infrastructure, training, or guardrails each team needs to build effectively on their own.
• Build Reusable AI Capabilities: Prototype agentic workflows, Claude skills, MicroApps, and retrieval features on our enterprise stack (Claude Enterprise, MCP, APIs), always choosing the lightest technical pattern that supports the behavior we want. Package what works so another market can adopt it without rebuilding it.
• Create the Enablement Infrastructure: Help create the templates, playbooks, starter kits, and documentation that let teams self-serve: how to build a skill, an Artifact, a plugin, etc.;