AI Builder Intern

Scale AI•San Francisco, CA

7h•$74,400 - $111,600•Remote

About The Position

This isn't a research internship. You won't spend the summer writing reports or sitting in strategy meetings. You'll spend it building — shipping AI-powered tools, automating workflows, and deploying agentic systems that real teams at Scale AI use every day. Embedded in the Data & Technology org, you'll work directly alongside engineers, data scientists, and ops leads on live automation initiatives. If you have strong instincts for what AI can do today, a bias for building over theorizing, and a fluency in modern LLM tooling — this role is for you.

Requirements

Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) — you've built something real with them
Comfortable with Python and/or JavaScript; able to read and write production-quality code
Familiarity with at least one agentic or automation framework (LangChain, AutoGen, n8n, Zapier + LLM, etc.)
Strong product instincts — you think about who will use what you build and why it matters
Able to move fast without breaking things that matter; iterative, scrappy, but deliberate
Currently enrolled in an undergraduate or graduate program in CS, data science, engineering, or related field

Nice To Haves

Prior internship or project experience in a BizOps, RevOps, or enterprise automation context
Experience integrating Slack, Salesforce, or finance/ops systems via APIs
You've built something with a multi-agent architecture — even a side project counts
Familiarity with prompt engineering, RAG pipelines, or LLM evals
An active GitHub, portfolio, or side project that shows what you can do

Responsibilities

Design and deploy multi-step agentic workflows using LLM-integrated frameworks (LangChain, LangGraph, CrewAI, or similar)
Build API-connected automations that tie together internal tools — Slack, Salesforce, Notion, and internal data systems
Prototype and iterate fast; build things that work, then make them better
Develop lightweight internal tools and dashboards that surface AI outputs to business teams
Vibe-code functional UIs — React, plain JS, or whatever gets to working fastest — for internal adoption
Identify friction points in current workflows and propose AI-first replacements
Instrument your own work — capture usage signals, time-saved estimates, and adoption metrics from day one
Contribute to the org's ROI measurement framework by tagging your projects to defined value categories