Moonbug Labs Graduate Program

About Moonbug Labs

Moonbug Labs is Moonbug Entertainment's internal innovation function, built to move quickly on ideas that help the rest of the business work better with data and AI. We sit inside Platforms & Music, and our job is to turn the scale of Moonbug's business across shows like CoComelon, Blippi, Little Angel and Morphle into things the whole company can use: data, tools and AI systems that take on real tasks.

We don't build one kind of thing. In any given week the team might ship a data pipeline, stand up a dashboard, prototype a AI workflow, automate a manual process, or build an operational tool that a whole department ends up relying on. What ties it together is the stack rather than the output: large language model (LLM) agents, retrieval and knowledge systems, analytical data infrastructure (we run heavily on ClickHouse and BigQuery), and tooling built on the Model Context Protocol (MCP), which lets AI agents connect to the systems we actually use.

We're a small team that ships fast and prototypes in days rather than weeks. Rough but working beats polished and untested.

About the Program

This is a graduate program for people at the start of their career, whether you're coming straight out of a degree or deliberately changing track into tech and data. We hire for attitude first. We'd rather take someone entrepreneurial, curious and quick to figure things out than someone who ticks every technical box on paper but waits to be told what to do.

We're also not looking to hire five of the same person. The team has a mix of strengths and we want the program to reflect that. Some people who join us will lean toward software engineering. Others will head for data engineering, infrastructure and DevOps, BI and analytics, security, or building AI agents specifically. If one of those disciplines excites you and you want to go deep, that's a great fit. If you're a generalist who wants a bit of everything, that works too. What matters is the energy and drive you bring to whatever you pick up.

What you'll actually be doing

• Working on AI agent tooling: building and integrating connectors that let AI agents reach more of the systems and data sources across the business.
• Rapidly prototyping internal tools: taking a rough idea (a dashboard, an alert, an agent that automates something manual) from sketch to working demo in days, not weeks.
• Turning manual processes into reusable tools: spotting repetitive, one-off work across teams and building a lightweight fix, whether that's a script, an internal app or an AI agent.
• Working directly with data pipelines: querying and validating data in our warehouse and document stores, helping debug ingestion issues, and building lightweight data quality tooling.
• Prototyping and iterating on AI agents: contributing to pieces like extraction logic, evaluation harnesses, and guardrail and access-control checks, under the team's guidance rather than owning a full production system solo.

Requirements

What we're looking for

We care far more about how you think and work than about ticking every box below, but there is a baseline we're looking for:

Core ability

• Comfortable reading and writing code, with Python and/or JavaScript/TypeScript the most useful to us, and enough fundamentals to debug something rather than accept whatever a tool hands you.
• Solid SQL fundamentals (joins, aggregations, window functions). A lot of our work sits on top of a data warehouse, so this matters whichever discipline you lean toward.
• Some familiarity with git and version control, and an instinct for keeping your work reproducible rather than stuck on your local machine.

Comfort with modern AI tooling

• Hands-on experience building with AI coding tools such as Claude Code or Cursor. It doesn't need to be professional experience; side projects and scrappy builds count.
• Enough prompt engineering knowledge to get reliable output, and the judgement to check AI-generated work for correctness and mistakes rather than shipping it blind
• Familiarity with concepts like embeddings, retrieval-augmented generation (RAG), vector search, agents and context windows.

Good to have, not required

• A basic sense of how data warehousing works
• Awareness of API integration patterns and the basics of OAuth and access control
• Interest in or experience with the YouTube platform and its APIs, or with music and rights data. Given the space we work in, this is a real plus.
• Any interest in or exposure to media, entertainment or children's content data
• Comfort with agent- and connector-driven ways of working, since much of what we build isn't a classic linear pipeline

How you like to work

• You'd rather ship something rough and iterate than polish a spec no one has tested.
• You bring your own drive and ideas instead of waiting for a fully specified brief.
• You're comfortable with ambiguous or half-written requirements, and your instinct is to ask a good clarifying question rather than stall.

We're looking for curiosity, initiative and an entrepreneurial streak, not a finished specialist. Whatever your strengths across engineering, data, AI or analysis, if you're excited to build, we'd love to hear from you.

- Comfortable reading and writing code, preferably in Python and/or JavaScript/TypeScript, with fundamental debugging skills. - Solid SQL fundamentals (joins, aggregations, window functions). - Familiarity with Git and version control for reproducible work. - Hands-on experience building with AI coding tools (e.g., Claude Code, Cursor). - Enough prompt engineering knowledge to get reliable output and verify AI-generated work. - Familiarity with concepts like embeddings, RAG, vector search, agents, and context windows.