Ask HN: Hiring in the age of AI-assisted coding: what works?

> They started getting inverted signals because the hardcore vibecoders knew all the tricks to brute force the problem with high token spend. They'd do things like spend the interview trying to spin up parallel subagents to brute force a solution.

Can’t you just…tell them not do this? Or give them limited model access instead of full Claude Code / Codex?

You should interview for the skill that is required in the age of AI-assisted coding.

That is, looking at code that's been written by AI and see what's wrong, what's superfluous and what's missing.

For preparing the code for the interview, I would suggest prompting Claude Code using a requirements document that's purposefully a bit vague so that the AI will have to make choices when writing that code.

When you have the interviewee come over, show them the code and have them criticize those choices and edit the code manually (I know) so that they can demonstrate that they can intervene in the AI process at the correct inflection points.

When I find problems at that AI does a bad job at, I take note, and those are exactly the kind of problems I give in a take-home assignment + interview.

If the candidate turns in AI slop and doesn't understand the fundamentals of what they're working on, reject. If they took the time to learn the subject matter and feed it their own ideas to improve the output, awesome.

The signal I’d care about is whether they can scope the task, prompt the model, review the output, explain tradeoffs and clean up what the model got wrong.

I've recently been tasked with hiring new college grads (less than 2 years of experience) AI Engineers. Some things that we've been doing:

1) Do they understand the ecosystem of algorithms and models? How do those coexist? Statistical models are the right choice sometimes. Sometimes it's XGBoosted RFs, sometimes NNs, LLMs, etc. And they're not mutually exclusive. I don't think that has changed since AI to be honest - though I get bad candidates that say LLM-everything of course.

2) AI-assisted fluency. Not just in coding, but in concept build. I don't expect them to have the velocity of an AI-fluent principal engineer, but I want to see that they're not resistant to AI-assistance. This is obviously new.

3) Experience with production systems, more than before. Pre-AI, I'd accept that a recent grad might be tuned towards models and algorithms, and wouldn't know much about frontend or backend, or anything you run into in production environments. Given the ease with which you can now setup a small DB, your modeling pipeline, and a full react dashboard or fastAPI frontend...I'd at least like to see they've dabbled in all of that, have a rough sense of it. I don't need them to be full-stack, or even comfortable with it - but AI has raised the breadth bar for me.