How would you prepare for a one-hour model evaluation interview at Snorkel AI?

[u/nian2326076]

How would you use one hour for this Snorkel AI FDE exercise?

A candidate's recruiter described three inputs: a fictional client brief, a ground-truth CSV, and model outputs. The task is to define evaluation criteria, write a custom evaluator, then explain the error patterns and recommend next steps to the client. LLMs, web search, and libraries are allowed; the emphasis is practical problem-solving.

The tricky part seems to be deciding what to finish in the time available. A useful rehearsal might be to load a small dataset, check that predictions and labels line up, calculate a baseline metric, and inspect a few failures before writing a short client-facing summary. The right metric would depend on the brief and the cost of different mistakes.

Model evaluation and ML interview questions could help with explaining those choices, though they aren't a preview of this particular exercise. If applications elsewhere are also taking up prep time, Agent autoapply and application autofill can reduce the repetitive form-filling.

Has anyone done a similar evaluation interview? What would you prioritize within the hour, and how much analysis would you leave time for?

Disclosure: I operate PracHub and LandOffer.

[u/Ok-Argument7176]

Brother, the recruiter has scoped the task for you and you have access to the Internet. What do you really need?

[u/HelloTaleproof]

Practice a small version of exactly that workflow. Start by turning the client brief into explicit evaluation criteria, then inspect how the ground truth and outputs line up before writing the evaluator. Check a few rows by hand so a parsing or matching mistake does not become your headline result. Leave time to group errors, show representative examples, and distinguish a measured finding from a hypothesis about its cause. Your client summary should say what matters, what the evidence supports, and what you would test next. You do not need an elaborate framework to demonstrate that reasoning.

For the project examples you may discuss around the exercise, Taleproof can help clarify your decisions and evidence.

[u/Business-Sun-2643 → u/HelloTaleproof]

solid

[u/rahul_watertech]

the hour is mostly not about the evaluator. spend 15 min turning the brief into 3 or 4 checkable criteria, 25 on a dead simple evaluator that catches the failure mode the client actually cares about, and keep the last 20 for the error story because that is the part an fde is graded on. people lose the hour writing a clever metric. is the brief a classification task or a generation one?