AI Product Engineer at Notion — building the LLM-powered Custom Agents that automate recurring team workflows

This role builds the AI-powered features behind Notion's Custom Agents, which automate recurring workflows like filing tasks, writing reports, and answering knowledge-base questions. It's framed as an 'AI Product Engineer' position emphasizing prototyping with LLMs and embeddings, productionizing async workflows at scale, and cross-functional collaboration with AI, Infrastructure, and Product teams. The JD does not specify a hard years-of-experience floor for the core bar, though the nice-to-haves reference 10+ years as a stretch signal, so this reads as a mid-level bar with room for a strong generalist.

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What this interview tests

Building AI product features using LLMs, embeddings, and other ML technologies
Productionizing and scaling asynchronous workflows
System design from UI through data model, with relational databases (Postgres/MySQL)
Decomposing ambiguous problems into clean, shippable solutions
Cross-functional collaboration with AI, Infrastructure, and Product teams
Communicating technical decisions clearly to varied audiences

Common question themes

Walk through an AI feature you built end-to-end using LLMs or embeddings, from prototype to production

How did you scale an asynchronous workflow that started as a quick prototype

Describe a time you had to decompose a genuinely ambiguous problem into a concrete plan

Tell me about a technical decision you had to explain clearly to a non-engineering stakeholder

How do you think about the real-world impact of an AI feature on users, good and bad

Describe a disagreement with a teammate about technical approach and how you resolved it

How candidates describe it

Real Software Engineer interview stories — retold from candidates' public write-ups, with sources.

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