GTM Engineer Search

AI policy for GTM engineer interviews

Allow AI in any round that produces an artifact, and ban it in the round that asks what the candidate already did. A case study or a take-home covers work they would do with a model open once they are in the job, so running it without one tests a version of the role nobody holds. The behavioral round is the exception. It asks what already happened, and no model was there.

Hold with AI open 23
Need proctoring 8
Bank version 2026.7

Should candidates use AI in a GTM engineer interview?

Yes in every round that asks them to build something, and no in the round that asks what they already built. That is the whole rule, and it follows from what the job actually is.

The taxonomy publishes four ways these people build, and two of them (AI-assisted and AI engineering) describe someone who works with a model open. A case study brief that bans AI asks the candidate to work in a way they will never work again after the offer, and then scores the result.

The behavioral round runs the other way. Nothing gets made in it. You are asking what happened on a system the candidate ran, and a model can only help them sound like the person it happened to.

What each round does about AI

The rule reads differently in each round because the rounds are asking for different things. Where an invitation leaves it unstated, candidates take the permissive reading, so write the policy into the invitation.

  1. 01

    Behavioral: no

    Nothing is produced here, so there is nothing for a model to produce. The questions ask what the candidate built, what broke, and how they found out, and an answer drafted live is an answer about somebody else's system. Ask them to close it, then read the follow-ups for the point where a prepared answer runs out of specifics.

  2. 02

    Technical: allowed, with the recall questions anchored

    Allow it, and change what you ask instead of what they can open. Two questions in our behavioral set ask for a fact any model has: how to set up a cold-email domain that also carries the team's day-to-day mail, and how to re-run a bad enrichment day without duplicating rows or paying twice. Both have a right answer, and it is in the training data.

    Two more ask for facts just as checkable and get a model nowhere: the DNS records on the last sending domain the candidate set up themselves, and the per-inbox daily volume the last program they ran carried. Same kind of question, opposite exposure, because the second pair is anchored to a system only this candidate operated.

    So keep the recall questions and anchor them. Ask what they set, on which domain, and how they arrived at the number, rather than what the number should be.

  3. 03

    Case study: yes

    The candidate is producing an artifact under the conditions they would produce it at work, and those conditions include the models they would use. Banning AI gets you a slower version of the same deliverable. Score what they took from the model, what they threw out, and whether they questioned the brief you handed them or executed it as written.

  4. 04

    Portfolio review: yes

    A portfolio walkthrough covers work that already exists, so there is nothing to police. If a model wrote part of it, that is a fact about the work and worth asking about. Ask which parts it wrote, what the candidate checked before shipping, and what happened to the thing after it went live.

  5. 05

    Reverse questions: does not arise

    The candidate is asking you here, so there is no policy to set. What they ask about AI is still information. Someone who wants to know which parts of the stack you expect a model to run, and who reviews its output, is asking a question you only think to ask after owning one.

Why it depends on how they build

The taxonomy sorts GTM engineers by approach as well as by capability domain, and the approach a role wants decides how much an AI-allowed exercise tells you. There are four approaches, and the AI question lands differently on each.

  1. 01

    No-code automation

    The work here is wiring platforms together (Zapier, Make, n8n, Clay), with no codebase underneath. A model can draft the logic, so an AI-allowed exercise tests whether the candidate knows what that logic does at volume, on a bad record, and when a vendor changes its schema. Ask them to explain a step they did not write.

  2. 02

    AI-assisted

    Here the work gets done through models inside software the candidate already runs — a generated column in a tool they own, or a research pass they never wrote by hand. Taking AI out of a round for a role like this removes the thing you are hiring for. Watch what they prompt, what they keep, and what they hand over without reading.

  3. 03

    AI engineering

    The candidate builds the model into a system that runs without them, with retrieval, prompts under version control, and evaluations that catch a regression before a prospect sees it. An AI-allowed exercise is the only kind that tests this at all. The tell is whether they can say how they would know the thing had degraded.

  4. 04

    Custom code

    The role expects real programming, in Python or TypeScript or Go, for services and integrations nobody sells off the shelf. A model writes a plausible first draft of most of it, so the exercise is whether the candidate can read the draft. Ask what they changed before it ran, and what they deleted.

The questions that stop working when a model is open

You cannot enforce a ban in a remote round. You can ask for one, and then you assume it held. These are the questions in the behavioral set that quietly stop working when the assumption is wrong. Each puts a scenario to the candidate instead of asking what they did, and a model answers a scenario competently.

That is a reason to place them carefully, not to drop them. They work in a room, on a shared screen, or as the opener to a conversation that then turns to the candidate's own history. Each one carries the follow-ups that push past a generic answer.

  • 02 A funnel you own is leaking between acquisition and activation. What are the first three things you do? Whether the first reflex is diagnosis or tooling

    Follow up with

    Strong answer

    A strong candidate treats the stated leak as somebody's hypothesis and starts by asking what it is based on. The first move is questions rather than actions: which segment is leaking, whether it has always leaked or started recently, and whether the activation event is instrumented well enough to say. They usually get to whether the people arriving were the right people at all, which moves the problem out of the funnel and into targeting.

    Weak answer

    A weak candidate accepts that the leak sits between acquisition and activation because the question said so, and answers with tools they would add: a session recorder, a warehouse, a dashboard. Nothing in the three moves would tell them the premise was wrong.

  • 03 You are handed a cold-email domain that also needs to run the team's day-to-day mail. How do you set it up? Whether deliverability knowledge is operational or repeated

    Follow up with

    Strong answer

    A strong candidate stops on the premise, because a domain's MX records point at one provider and the setup as described does not exist. They separate the sending domain from the corporate one, usually a lookalike that redirects, and only then talk about authentication and the ramp. Ask for the ramp week by week and they give you weeks and numbers rather than a principle.

    Weak answer

    A weak candidate takes the premise at face value and starts configuring inboxes and warmup on the corporate domain. The conflict is sitting in the question, and reciting a sound warmup ramp on top of it does not make the domain safe to send from.

  • 23 Your CRM says a campaign produced 40 opportunities. The attribution report says 12. Where do you look? Whether they reach for definitions before dashboards

    Follow up with

    Strong answer

    A strong candidate goes at the definitions before either system, because both numbers can be right under their own rules: which object counts as an opportunity, what window, first touch or multi-touch, how duplicates were merged. They will ask what the number is for before choosing which one to take to the board. A good answer ends with the rules written down where both teams read them, since this argument returns every quarter.

    Weak answer

    A weak candidate assumes one of the two systems is broken and starts checking the integration. A gap between 40 and 12 is wide enough to be a definition rather than a sync failure, and that possibility never gets raised.

  • 24 A vendor returned garbage for yesterday's enrichment run and you need to re-run it. How do you do that without duplicating rows or paying for the same records twice? Whether re-running a job is routine for them or something to avoid

    Follow up with

    Strong answer

    A strong candidate replaces the affected day rather than appending to it, and makes the write idempotent on a stable key so running it twice lands the same rows. Ask what happens when the re-run dies halfway and the answer is that they start it again, which is the point of having built it that way. Expect a concrete check that the day is now right, usually a count plus a sample compared against what the vendor returned.

    Weak answer

    A weak candidate deletes rows by hand, or re-runs the job and plans to clean up the duplicates afterwards. The question names both costs it is protecting against, and paying the vendor a second time for records already bought does not come up.

  • 25 A sequence that was replying at four percent drops below one in a week. Nothing about the copy changed. Walk me through it. Whether they reach for infrastructure or for rewriting

    Follow up with

    Strong answer

    A strong candidate uses the sentence about the copy, and treats copy as the last suspect precisely because it did not change. They go at delivery first: authentication, domain reputation, a seed test, and whatever else moved that week in volume, provider or list source. Expect them to hold the answer open, since an unchanged-copy collapse can be placement, a worse list, or a shift in who is being mailed, and a week is not long enough to have separated those.

    Weak answer

    A weak candidate starts rewriting subject lines, which the question rules out in advance by saying nothing about the copy changed. Or they blame list quality without first establishing whether the mail is arriving at all.

  • 26 Inbound demo requests are reaching a rep 30 hours after they come in. The SLA is five minutes. Where do you look first? Whether they can decompose a routing path in order

    Follow up with

    Strong answer

    A strong candidate walks the path in order and says it out loud: form submission, record creation, assignment rule, queue, notification, whether a rep was awake. Thirty hours against a five minute target is a stop somewhere rather than slowness everywhere, and they are hunting for the step where the clock parks. What they want afterwards is a timestamp at each hop, so the next slip shows up as a number rather than as a complaint.

    Weak answer

    A weak candidate proposes a new routing tool, or decides the reps are ignoring their alerts, before knowing whether the lead reached one. The gap in the question is wide enough to locate precisely, and nothing in the answer would locate it.

  • 27 You are four days into a test of a new outbound sequence, the variant is replying 30 percent better, and a VP wants it rolled out to the whole list on Monday. What do you say? Whether they can hold a line on evidence without stonewalling

    Follow up with

    Strong answer

    A strong candidate explains in plain language why a four-day read moves around, without leaning on the word significance, and goes to what was agreed before the test started, including the case where the answer is nothing. Then they give the VP a route: what they would need to see to call it on Monday, or a partial rollout that keeps the test alive. Expect them to say what would make them happy to call it early, because sometimes the gap is wide enough and the downside small enough.

    Weak answer

    A weak candidate concedes on the spot because a VP asked, or lectures about sample size and offers nothing the VP can act on. Either way the person who has to decide on Monday leaves with the options they walked in with.

  • 28 A prospect receives four emails in one day from four different systems. How do you stop that happening again? Whether they fix the instance or the architecture

    Follow up with

    Strong answer

    A strong candidate reads four systems as the problem rather than four emails, and puts the decision to send behind one arbiter holding suppression and frequency for all of them. They will name which system that should be and why, usually the one every tool already syncs through. The harder half is the second follow-up, and a good answer says how the next tool somebody buys inherits the rule instead of routing around it.

    Weak answer

    A weak candidate turns off the offending campaign, or proposes a standing meeting between the teams that own the four systems. The question asks how it stops happening again, and a fifth system could be bought next month under either answer.

Candidates already grade take-homes against AI

One candidate wrote up a GTM engineer take-home they had just failed. Before submitting it, they had role-played the hiring panel with several models against the real brief, taken the critique, revised, and repeated that until every model came back with hire. Then they sent it.

Assume any take-home a model can pre-grade is being pre-graded before it reaches you, whether or not the invitation said AI was allowed. An AI-allowed exercise describes what is already happening. The design question that follows is what stays hard once a model has read the brief: an onsite where a technical person attacks the architecture, and a defense of the call the candidate made when the brief and the real problem pointed in different directions.

Frequently asked questions

Should candidates be allowed to use AI in a GTM engineer interview?

Yes in any round that produces an artifact, and no in the round that asks what they already did. A case study, a take-home, and a portfolio walkthrough all cover work that gets done with a model open on the job, so an exercise that bans it measures something the role never asks for. The behavioral round is the exception, because nothing is built in it and the questions are about a system the candidate ran.

How do I stop candidates using AI in a remote interview?

You mostly cannot, so design the round on the assumption a model is open. Screen sharing and proctoring raise the cost of hiding it, and neither settles it. The move that works is asking questions a model cannot answer for them: what this candidate configured, on which system, at what volume, and what happened when they got it wrong.

Does an AI-assisted take-home still tell me anything?

Yes, if you score the judgment rather than the output. Every deliverable comes back more polished with a model, so the separation moves to what the candidate questioned in the brief, what they rejected from the model, and what they can defend when someone technical attacks the architecture. Add a session where they walk through their choices out loud.

Which interview questions still work if the candidate has ChatGPT open?

The ones anchored to what this candidate personally did. Of the 31 behavioral questions in our bank, 23 hold with a model open, because they ask for specifics only the person who ran the system has: the DNS records they set, the per-inbox volume they ran at, and what broke after they left. The other 8 put a scenario to the candidate, and a model answers a scenario well.

Next step

Hiring a GTM engineer?

Are you a GTM engineer?

Get on the radar