Machine Learning Engineer interview loops

A machine learning engineer — ML engineer, in most job ads and most searches — is not hired on knowing which algorithm to reach for. The work is a model that is a little wrong about everyone and very wrong about some people, wired into a product that acts on its scores before anyone has checked them. The job is to know how wrong, to keep the offline world and the online world telling the same story, and to make the model's mistakes reversible. That is ordinary engineering discipline applied to a component whose correctness cannot be read from its code, and it is what the rounds below are written to read.

Two things make the role hard to interview for. The first is that the textbook material is abundant and fluent, so a round built on definitions measures a course, not a career. The second is that the failures worth hiring against are all disagreements between two things that should match: a feature computed one way in training and another way at request time, an offline metric that rises while the product gets worse, a retrained model whose scores move without anyone downstream being told. None of those is visible in a whiteboard answer, so each round here hands the candidate an artifact — a repository, a data pack, an experiment readout, an incident 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.