New-grad AI engineer role at Notion — ship a scoped AI feature and prove it with evals, not just a demo

Notion is hiring new-grad engineers into one of several AI-focused teams — AI product engineering, model/systems engineering, or evaluation & quality — to help build model-powered features into Notion's workspace. The role expects a candidate straight out of school (or with internship experience) to prototype an AI feature, take ownership of a scoped productionization project, and contribute to evaluation frameworks that measure model/product quality.

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What this interview tests

Prototyping and shipping an AI-powered feature (LLMs, embeddings) end to end
Productionizing a model integration: monitoring, guardrails, latency/cost/reliability
Building or extending evaluation frameworks (evals) to measure model/product quality
Working across UI, APIs, and data (familiarity with Postgres/MySQL)
Breaking down ambiguous problems and iterating from teammate feedback
Communicating and collaborating cross-functionally (product, design, data)

Common question themes

Tell me about a project where you integrated an LLM or embeddings into a real feature

How would you productionize an AI prototype — what guardrails or monitoring would you add before shipping

Describe how you'd build an evaluation set to measure whether a model change actually improved things

Walk through a time you had to learn an unfamiliar part of a stack (UI, API, or DB layer) to ship a feature

How do you approach an ambiguous, loosely-specified AI feature request

Tell me about incorporating feedback from teammates on early/rough work

How candidates describe it

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

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