MCP & Memory Intern

InternHunt; Boston, Northern (MA, KY); Hybrid; USD 22,000 - 33,000; Full time

Job summary

DataRobot in Boston is offering a hands-on AI internship focused on developing agent memory, MCP integration, and tool coordination. You will write code, test ideas, and ship improvements in a fast-moving environment.

This role emphasizes curiosity, self-direction, and clear communication over a perfect resume. No expert level required; you’ll learn by doing and collaborating with teammates to unblock progress.

Qualifications

• Familiarity with Python and willingness to improve quickly.
• Curiosity about how AI agents work: tools, prompts, context windows.
• Interest in MCP — servers exposing tools and contract simplicity.
• Interest in memory for agents: what to store and retrieve.
• Ability to take vague problems, try a small version, and learn.
• Clear communication: say when stuck, share attempts, help others move faster.
• Good engineering habits: readable code, small tests, and notes for the next person.

Responsibilities

• Work on MCP and memory at the center of agent systems.
• Connect agents to tools and shape how context is passed.
• Design how an agent remembers what is useful and when to forget.
• Write code, try ideas, test what breaks, and ship improvements quickly.

Skills

Python

AI agents

MCP

Agent memory

Problem solving

Communication

Testing

Readable code

Job description

Job Description:

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.

This effort is about making AI agents more useful in real work: giving them the right tools, the right context, and a memory that holds up across a conversation and across sessions. The focus is Model Context Protocol (MCP) and agent memory, how an agent discovers tools, calls them safely, keeps track of what it has learned, and stays helpful without someone watching every step. The goal is practical value for people using agents day to day, with clean implementations that others can build on.

Summary:

This is a hands-on internship for someone curious about AI agents who wants to learn by building. You do not need to already be an expert. What matters is that you like figuring things out, you move without waiting to be told every next step, and you care about unblocking the people around you. Most of the work sits on MCP and memory: connecting agents to tools, shaping how context is passed in, and designing how an agent remembers what is useful and forgets what is not. You will write code, try ideas, see what breaks, and ship improvements in a fast-moving environment. A good attitude and the willingness to learn matter more than a perfect resume.

Required Skills:

• Comfort writing and reading Python, and a willingness to get better at it quickly.
• Curiosity about how AI agents work: tools, prompts, context windows, and why an agent succeeds or fails on a real task.
• Interest in MCP — how servers expose tools, how clients call them, and how that contract stays simple and reliable.
• Interest in memory for agents: what to store, what to retrieve, and how that changes what the agent can do next.
• Ability to take a vague problem, try a small version, and show what you learned.
• Clear communication: you say when you are stuck, you share what you tried, and you help other people move faster.
• Good engineering habits you are willing to practice: readable code, small tests, and notes so the next person is not lost.
• You take feedback on board. When someone points out a better approach, a mistake, or a simpler path, use it and adjust.

Strong Plus:

• You have built or used an MCP server or client, even a small one.
• You have played with agent frameworks or tool calling.
• You have thought about memory, caching, or conversation state in any product, not only in AI.
• You enjoy fast environments and would rather try something today than wait for a perfect plan.
• You like helping teammates get unblocked, even when the task is not yours.
  • What You Can Expect To Work On:

  What You Should Expect:

  Work close to how agents actually get used, with MCP and memory at the center.

  • A fast pace: short loops, quick feedback, and room to try things without a long approval chain.
  • Trust to drive your own tasks once the goal is clear. You will not be micro-managed.
  • A team that likes curiosity, a steady attitude, and people who make others faster.
  • Room to learn in public: questions are welcome, and “I don’t know yet” is a fine starting point if you go find out.
  • A role for someone who wants to grow into agentic systems by shipping, not by waiting until they feel fully ready.