"Frontend engineer role, AI model training" sounds vague until you realize what it actually means: you're not writing code, you're judging someone else's, except the someone else is a language model.
Companies like Mercor, Turing, and Surge AI hand experienced React/Next.js engineers model-generated code and ask them to decide if it's correct, safe, and idiomatic, then explain exactly why in writing. That last part matters more than people expect. You can spot a bug perfectly and still fail the assessment if your written explanation is vague.
The technical round runs two to three hours and centers on real scenarios, not trivia. Two patterns show up constantly: hydration mismatches (server and client rendering different values) and misplaced client boundaries (a whole page marked "use client" when only one button needed it). Both are common mistakes in AI-generated Next.js code, and both are very learnable if you know what to look for.
There's also a rating dimension most people don't prepare for at all: severity calibration. Flagging a style nitpick as critical, or missing a real correctness bug while praising formatting, tanks your accuracy score fast.
I put together a full breakdown of the exact code scenarios, sample evaluation walkthroughs, and a realistic weekend prep plan here: https://devencyclopedia.com/blog/mercor-frontend-engineer-interview-prep
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