ML / AI Infrastructure Engineering Intern — Summer 2026
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About this role
About the role
Arcline builds AI-powered data tools that help K-12 school districts turn fragmented student data into clear, actionable decisions. We work with superintendents and district leaders across Alabama, California, Kentucky, Texas, Wisconsin, and more — replacing months of manual reporting with instant, AI-driven answers.
We're a small, AI-native team that ships fast and builds with real users. Our interns ship real features to real classrooms.
You'll work on the core intelligence layer that powers Arcline's natural language query engine — the system that lets educators ask questions in plain English and get accurate, cited answers from their district's data.
Day to day
- Build and improve RAG pipelines and agent workflows using LangChain, LlamaIndex, and vector databases (pgvector, Pinecone)
- Preprocess and normalize messy education data from multiple district sources for use in ML pipelines
- Evaluate retrieval quality, answer accuracy, and prompt performance across real district datasets
- Build evaluation harnesses and benchmarks to systematically measure and improve model output
- Work with the founding engineering team to ship improvements directly to production
Requirements
- Currently pursuing a B.S./B.A. or M.S. in Computer Science, Machine Learning, Data Science, or a related field
- Familiarity with ML concepts — you've taken coursework or done projects involving embeddings, retrieval, or model evaluation
- Proficiency in Python
- Able to commit to a 10-week internship from June 1 to August 10, 2026. Based in San Francisco with remote positions also available. Relocation assistance provided for on-site roles
Bonus qualifications
- Experience with LangChain, LlamaIndex, vector databases, or LLM APIs (OpenAI, Anthropic)
- Experience building RAG systems or agent-based workflows in production or side projects
- Familiarity with evaluation frameworks, prompt optimization, or fine-tuning
- Coursework or research in NLP, information retrieval, or knowledge graphs
- Experience with data preprocessing, feature engineering, or working with noisy real-world datasets
- Experience with Postgres, FastAPI, or data pipeline tools (dagster, dbt)
- AI-native development habits — you use LLM tools like Cursor, Claude Code, GitHub Copilot, Codex, or anything else to write, debug, and ship code faster