Most engineering interview loops weren't designed. They accreted. Someone added a take-home because a bad hire got through without one. Someone added a system-design round because a candidate looked great in coding and floundered in production. Someone added a "culture fit" chat because a strong performer turned out miserable to work with. Years later you have six rounds, three duplicating each other, one measuring nothing AI hasn't automated, and nobody who can explain why the loop is shaped the way it is.
That's the case for redesigning the whole thing at once, not patching it round by round. A loop is a system — each stage should test something the others don't, in ascending order of cost, and the sum should predict who's good at the job. AI is what exposed how badly most loops fail that test: rounds that used to be redundant-but-harmless are now actively misleading, rewarding the wrong thing louder than before. Below is what a loop looks like designed from the job backward, instead of from interview-round tradition forward.
Principles first
Three principles do most of the work, and everything below is an application of them.
Measure real work. Every stage should look, as closely as logistics allow, like something the person will actually do on the job — read a codebase, extend it, explain a decision, disagree with a teammate. A round that measures something adjacent to the job (recall a textbook algorithm, whiteboard a system nobody will build that way) is a proxy, and proxies degrade. AI degraded the classic ones fastest, because closed, context-free problems are exactly what a model solves best on a candidate's behalf.
Respect candidate time. Every hour you ask for is an hour a candidate with options could spend elsewhere, and your best candidates always have the most options. A loop that's expensive to complete filters for people with nothing better to do, at the exact moment you want people with better offers. Time cost isn't a candidate-experience nicety — it's a selection bias you introduce by ignoring it.
Keep every stage defensible. If you can't state, in one sentence, what a round measures and why that predicts performance, cut it or fix it. "We've always run a whiteboard round" is not a defense. "This tests whether a candidate can read an unfamiliar function and predict its failure mode, which is most of debugging here" is.
Run every later stage through those three filters and the loop below falls out naturally.
Stage 1 — a resistant first-round screen
The first round has to do the most work per minute, because it sees the most candidates. That's why it's the round AI broke hardest — closed, timed, well-specified was a defensible trade of precision for throughput, until a coding assistant could clear it as reliably as a strong human. The round now sorts on "has access to a good model," not on anything about the candidate.
The replacement keeps the volume-friendly properties — short, structured, gradable — while giving up the property that broke: closed-book isolation from the tool the candidate will actually use. Concretely: a small, real codebase, a task scoped to thirty or sixty minutes, an AI assistant explicitly allowed and visible, and a rubric that scores whether the candidate directed the tool or just accepted its first answer. That's what makes it resistant — an assistant can produce the diff, but it can't substitute for judgment about whether the diff is right, and judgment doesn't get easier to fake just because the artifact did.
This is a genuinely different exercise from a leetcode round or a take-home, and deserves to be built deliberately rather than retrofitted onto whichever one you already run. "Directed the tool or just accepted it" is the whole ballgame here, and it's specific enough to score directly — see our fuller rubric for it in how to evaluate AI-assisted coding skill.
Stage 2 — a work-sample or pairing round
If the first round is a scoped, timed filter, the second is where you spend real interviewer time on your surviving pool, and the deliverable changes: instead of "did they solve the task," you're asking "what's it like to work with this person."
The strongest version is live pairing on a small extension to a real codebase — forty-five minutes to an hour, AI assistant open the whole time, an interviewer watching and occasionally interjecting the way a teammate would. You're watching a process, not grading a finished diff: how the candidate reads code they didn't write, where they push back on the assistant, how they react when you change a requirement mid-task the way a product manager actually would.
Where live pairing doesn't fit — distributed teams, a role that wants a larger scope than an hour allows — a supervised or recorded work sample with a live follow-up defense gets most of the same signal at lower scheduling cost. We go deeper on that trade-off, and two other formats, in our piece on take-home alternatives. The throughline across all of them: realism, plus a way to verify the reasoning is the candidate's own, not the assistant's.
Stage 3 — a systems or judgment conversation
By the third stage you've confirmed the candidate can do the work. What's left is whether they can reason about it at a scope larger than one task — trade-offs across a system, not just correctness inside a function.
This is the round most loops already have some version of, and it's aged better than the others, because it was never an artifact-production exercise — it's a conversation. Keep it that way. The failure mode here isn't AI; it's turning judgment into a memorization contest, asking candidates to recite a scaling pattern read in a blog post rather than reason live about messy constraints. Anchor it in a trade-off close to what your team actually operates — consistency, latency, cost — and you get signal a rehearsed answer can't fake, because it doesn't bend when you change one constraint and ask what else changes.
Good candidates here ask clarifying questions before committing to a design, and can articulate what they'd give up under a different constraint. Weak candidates present one memorized shape and defend it regardless of what you change.
The debrief: evidence over vibes
The stage most loops under-invest in is the one after the last interview: the debrief, where people who each saw a slice of the candidate decide, together, whether to hire them. A good loop feeding a bad decision process still produces bad hires.
Two disciplines fix most debrief failures. First, structured rubrics scored independently before the group discusses anything — every interviewer submits scores against the same named dimensions before hearing anyone else's opinion. This is the single highest-leverage change available here: it stops the loudest or most senior voice from anchoring everyone else's read before they've formed their own.
Second, evidence over vibes: every score needs a specific, quotable moment attached, not a general impression. "Strong" isn't a data point; "caught that the cache invalidation missed the update path, and explained why before I asked" is. An interviewer who can't produce a specific moment gets their rating discounted, full stop — it forces the debrief to argue about what happened instead of how someone felt about it. We go deeper on where bias actually hides in a loop like this: see our take on interview fairness.
What to cut
A redesigned loop needs room, and the room comes from retiring stages that no longer earn their place — not shrinking every round by ten minutes and calling it fixed.
Cut redundant rounds first. If two stages both really test "can this person write correct code under light pressure," you have one round wearing two costumes; merge them. Write, for each round, the one sentence it's supposed to prove — any two rounds with the same sentence are a merge candidate.
Cut algorithm trivia specifically. It was a reasonable proxy when there was no cheaper way to filter volume; it's counterproductive now, because it's the most AI-solvable format left in most loops, and keeping it tells your strongest candidates your process hasn't caught up. We've made the fuller case for retiring it in our piece on whether leetcode interviews are still worth running.
Cut "culture fit" theater — the unstructured, no-rubric chat that exists to let one senior person form a gut impression. If collaboration matters, name it as a dimension with observable behaviors and score it inside a round built to surface it, like the pairing stage. An unstructured vibe check dressed up as a stage is where bias hides best, precisely because nobody has to justify the score with a moment.
A one-page example loop
Put the principles together and a loop for a mid-level engineering hire looks something like this. A forty-five-minute resistant screen: small real codebase, scoped task, AI assistant visible, scored on correctness and whether the candidate directed the tool or just accepted it. An hour of live pairing on a larger extension to the same codebase, scored on reading unfamiliar code, reacting to a changed requirement, and defending choices under follow-up. A forty-five-minute systems conversation anchored in a real trade-off your team actually faces, scored on the questions asked and how the answer bends when a constraint changes. Then a structured debrief: independent scores against named dimensions, submitted before discussion, each backed by a quoted moment.
That's three candidate-facing hours total, plus a disciplined debrief — less than most six-round loops ask for, and every stage measures something the others don't. You could run this with nothing but a shared repo, a video call, and a rubric doc. We built Chiron because designing and consistently grading a loop like this for every open role is real, ongoing work most teams don't have spare capacity for — see pricing if you want it packaged. But the design above doesn't depend on our product. It depends on treating your interview loop as something you design on purpose, and re-checking, on a schedule, whether every stage still earns its place.