AI Engineer interview loops

An AI engineer is not hired on knowing which models exist. The work is a feature that is wrong some of the time by construction, and the job is to know how often, to make the failure cheap, and to keep the rest of the product standing when the model is unavailable or has been talked into something. That is ordinary engineering judgment applied to a component nobody can make deterministic, and it is what the rounds below are written to read.

Two things make this role hard to interview for. The first is that almost every question about it has a fluent answer available from a blog post, so a round built on vocabulary measures reading rather than experience. The second is that the failures worth hiring against are quiet: a cache that serves a confident wrong answer, an eval suite that has been green for a month while support escalations climb, a tool that acts on instructions written by a stranger. None of those announce themselves, so each round here hands the candidate a real artifact — a pack of traces, a pack of prompt pairs, a repository, a ticket — and watches what they notice in it.

A loop is a set of round plans in order. Each round scores one to three competencies, every must-have is scored in at least two rounds, and the debrief is held competency by competency rather than round by round, so no single interview decides the hire. How these plans work covers the rest of the method.