Agentic AI R&D Intern — Summer 2026

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.

When an educator asks "which Title I schools have declining math scores and rising chronic absenteeism?" — that question can't be answered with a single database call. It requires decomposing the question into sub-tasks, routing each to the right data source, reasoning about how the pieces fit together, and synthesizing a cited answer. That's an agent orchestration problem, and it's the core of what you'll work on.

This is an R&D role. You'll prototype and evaluate multi-agent architectures that push beyond our current single-pass RAG pipeline — designing systems where specialized agents collaborate to handle the hardest queries our districts throw at us. You'll also explore proactive agent workflows: systems that autonomously surface funding gaps, compliance risks, and at-risk students before anyone thinks to ask.

You'll have mentorship from the founding team and room to learn — we're looking for curiosity and initiative, not a finished researcher.

Day to day

• Prototype multi-agent orchestration systems that decompose complex educator queries into coordinated sub-tasks across multiple data sources
• Implement tool-use and function-calling patterns that let agents query databases, generate reports, and cross-reference student records
• Build evaluation harnesses for agent reliability — testing that multi-step agent chains produce correct, cited answers
• Experiment with agent memory, state management, and context-passing strategies for multi-turn workflows
• Benchmark orchestration approaches (e.g., ReAct, plan-and-execute, hierarchical agents) against real district query workloads and share findings with the team

Requirements

• Currently pursuing a B.S./B.A. or M.S./Ph.D. in Computer Science, Machine Learning, AI, or a related field
• Strong Python skills and hands-on experience building with LLM APIs (OpenAI, Anthropic)
• Interest in and familiarity with agentic AI patterns — tool use, planning, multi-step reasoning, or multi-agent workflows
• A research mindset — you're comfortable running experiments, tracking metrics, and writing up what worked and what didn't
• 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

• Hands-on experience with agent frameworks — LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom architectures
• Projects or research involving multi-agent systems, agent planning, or autonomous AI workflows
• Familiarity with RAG systems, vector databases (pgvector, Pinecone), or retrieval-augmented agent architectures
• Experience with structured output, function calling, or constrained generation from LLMs
• Experience with agent evaluation and benchmarking — measuring reliability, tool-use accuracy, and reasoning quality
• Coursework or projects in NLP, information retrieval, or reinforcement learning
• Interest in education, public sector tech, or applying AI research to real-world product problems
• 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