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The Distyl AI
interview
Most companies are quietly anxious about candidates using AI. Distyl published its position instead: use it, and then be ready to defend everything it produced. That makes this the clearest public window into how AI-era interviewing actually works — and the anti-patterns generalise far beyond one company.
The published position
Distyl has published both how it runs technical interviews and how it thinks about AI-assisted interviewing.first-party2627 The core stance: AI assistance is permitted and expected, because it reflects how the work is actually done — and the evaluation shifts to judgement, verification and defence.
This is worth taking seriously beyond Distyl, because the direction of travel is obvious. When everyone can generate a plausible solution in ninety seconds, generating one stops being a signal. What remains as signal is the same thing FDE loops have always tested: did you decide the right thing, and can you defend it?
The same shift is visible on the practitioner side. Discussion at the 2026 AI Engineer World’s Fair described teams moving toward engineers writing the systems that write the feature code rather than typing it themselves.30 An interview that bans AI is testing for a job that is disappearing.
The loop, stage by stage
Distyl’s own account (July 2026). It says the mix is calibrated to your background, so your order and rounds may differ.
Take-home: a backend challenge, about two hours
AI is “fully encouraged”. Distyl says its problems come from real work, such as guardrails, caching layers, data retrieval and platform infrastructure, and one debugging exercise began as a bug in a production codebase.first-party26
Walk-through with an engineer
You present the take-home. Expect “what if this requirement changed?”, a pointed question about one section of code, and “why did you skip the guardrail?” Distyl calls this the stage with “the biggest differentiation between candidates”: a correct solution is not the bar, understanding why it works is.first-party26
Systems and product discussion, plus cases
“How would you approach building this from scratch?” at the level of a compound AI system rather than a single model call. Some stages add an ambiguous case study where “I don’t know, but here’s how I’d find out” beats overcommitting.first-party26
Logistics, from the careers page: interviews run over Google Meet, and engineering roles require three office days a week in San Francisco or New York.first-party51 The careers page says AI use is encouraged “at every stage”, while the October post carves out the codebase review; treat the specific post as authoritative and ask your recruiter, round by round. Distyl does not state a timeline or round durations, and does not give interview feedback.
| Stage | AI? | What to rehearse |
|---|---|---|
| Take-home | Yes | A guardrail, a cache or a retrieval step, with tests and a “how I used AI” note. |
| Walk-through | Not stated | Changing one requirement in your own code while someone watches. |
| Codebase review | No | A fixed checklist: correctness, failure paths, naming, tests, security, operability. |
| Live coding | Select screens | Narrating each prompt and why you accepted or rejected the answer. |
| Systems discussion | Not addressed | Decomposing a customer workflow into retrieval, tools, evals and a human fallback. |
What Distyl builds, and for whom
The company you are defending your design to has a particular shape. Know it before the systems round.
Distyl describes itself as an applied AI technology company that forward-deploys engineers and researchers inside large enterprises and also deploys its own products; its customers span telecom, healthcare, insurance, manufacturing and consumer goods.first-party52 Its case studies are anonymised — “F20 Healthcare Payor”, “F100 Telecom Operator” — with company-reported outcomes such as a telecom operator with more than 75% of interactions contained by AI. Treat these as marketing claims.first-party53 It was founded by Arjun Prakash and Derek Ho, both previously at Palantir, and reportedly raised a $175M Series B at a $1.8B valuation in September 2025.press54
Roles on its job board
- AI Engineer: hands-on builder; 5+ years, Python, evaluation frameworks and observability, 10–30% travel.52
- AI Strategist: project lead for the engagement and its business outcome, working beside a technical lead.
- Infrastructure Engineer, Enterprise AI Architect and Applied AI Researcher roles also appear.
What that means for your answers
- Frame designs as compound systems: models, prompts, tools, retrieval, evaluators, feedback loops.
- Say how you would measure and own the result in production; the posting says AI Engineers answer for production outcomes.
- Show comfort inside a customer’s constraints, not a greenfield.
Distyl suggests Chip Huyen’s Building a Generative AI Platform as the map of components its problems draw from. In practice that is guardrails, caching, retrieval and platform plumbing: guardrails, caching patterns and RAG are the on-site equivalents.
The three anti-patterns
Distyl names the failure modes explicitly. Each one is easy to fall into and each is visible within minutes of a defence conversation.
Shipping what the model produced without owning it
The code works, and you cannot explain why a particular decision was made because you did not make it. In a defence conversation this collapses instantly — every “why is it structured this way” returns a shrug.
Avoid it by: after every generated block, writing one sentence in your own words about what it does and why you kept it. If you cannot, delete it or understand it.
Accepting the model’s first framing of the problem
The model proposes an approach; you build it. Nobody ever asks whether the approach was right for this customer. This is the same failure as solving before scoping — the FDE cardinal sin — wearing a new costume.
Avoid it by: writing your own scope and constraints before the first prompt. Then ask the model for two alternatives and reject one out loud, in the write-up.
Building the impressive-looking thing, not the working thing
AI makes it cheap to produce breadth: five features, a nice interface, no error handling and no tests. It looks like more work and is worth less. For a deployment role this is the exact opposite of the required instinct.
Avoid it by: one path, complete, with failures handled and a way to prove it works. Then stop.
Use AI for generation; keep scoping, verification and judgement entirely human. Any artefact you cannot defend line by line is a liability rather than an asset, however good it looks.
How to run an AI-assisted take-home well
| Phase | Human | AI | Why this split |
|---|---|---|---|
| Scope | Entirely | None | What to build is the actual test. Outsourcing it fails the Default anti-pattern. |
| Design | Decide | Generate options | Asking for three approaches and rejecting two with reasons is a strong signal, not a weak one. |
| Implement | Review every line | Generate freely | This is where AI genuinely pays. Speed here buys time for verification. |
| Test | Decide what must be true | Write the cases | Choosing the properties to verify is judgement; typing assertions is not. |
| Verify | Entirely | None | Running it, breaking it, and confirming the failure modes is unbudgeable human work. |
| Write up | Your own words | None | A generated write-up is detectable and self-defeating — it is the artefact you will be questioned on. |
In the write-up, include a short section titled “how I used AI”: what you generated, what you rejected and why, and what you verified by hand. When AI use is expected, transparency about it is a positive signal — and it pre-empts the hardest question in the defence.
The defence conversation
The round that decides it, and the one that AI cannot help you with in real time.
Questions to expect
- “Why this structure and not the obvious alternative?”
- “What happens if this call times out halfway through?”
- “What did you decide not to build, and why?”
- “How do you know this is correct?”
- “Where did you use AI, and what did you reject?”
- “What would you change with two more days?”
How to prepare for it
- Write three trade-offs down while building — you will not remember them afterwards.
- Deliberately break your own code before the call and know how it fails.
- Prepare one honest weakness with the fix you would apply.
- Rehearse a two-minute overview: problem, scope, approach, evidence.
- Never say “the AI wrote that part” as an explanation. It is your artefact.
Take-home, defence and the rounds around it
The playbook has a timed spec, a rubric and a defence checklist; the interview hub covers the other eight round archetypes.
The picture: five stages, three different AI rules
Worked example: a two-hour caching take-home
Say the take-home is “add a response cache in front of the LLM calls in this support bot”. Caching layers are one of the problem areas Distyl names. The brief and the minute budget are illustrative. Here is the take-home split by the phase table above.
| Minutes | Phase | What happens | AI’s part |
|---|---|---|---|
| 0–15 | Scope | Write it down: exact-match cache with a TTL. Out: semantic cache, multi-region, a UI. | None |
| 15–30 | Design | Three cache keys proposed. Rejected: the raw prompt hash, because whitespace changes miss. Rejected: embedding similarity, because “cancel order 1234” and “cancel order 1235” are near neighbours with different right answers. Kept: normalised prompt + model + temperature + system-prompt version. | Generated the three options |
| 30–70 | Implement | Review every line. Caught: the generated code also cached error responses, so one timeout would be replayed to every user until the TTL ran out. | Generated the code |
| 70–85 | Test | You pick what must be true: a different model or prompt version is a miss, an entry expires on an injected clock, an error is never stored. | Wrote the cases |
| 85–105 | Verify | Stop the cache store mid-run. The bot should fall through to the model (fail open), not return errors. | None |
| 105–120 | Write-up | Scope, the two rejected keys, the error-caching catch, and a “how I used AI” section. | None |
The budget comes to 120 minutes. 50 of them (41.7%) are phases with no AI at all: scope, verify and write-up. The 40 minutes of generated implementation are only a third of the time. The two highlighted rows are your walk-through script. Each is a choice with a rejected alternative and a concrete reason. The error-caching catch in particular is exactly the line an Autopilot candidate ships without noticing, and it is the question you most want to be asked.
Cost and trade-offs: how to spend the AI
Here Time is what the AI use saves you, Space is what it leaves you to defend, and Why is the verdict.
| Case | Time | Space | Why |
|---|---|---|---|
| Generate the implementation, review every line | The most typing saved | Code you can explain line by line | Where AI pays. The time saved goes into verifying. |
| Generate three designs, reject two | Minutes | Two rejections with reasons: your defence script | The direct answer to the Default anti-pattern |
| Let the model frame the problem | The whole scope phase | A design nobody chose for this customer | The Default anti-pattern |
| Generate five features | Looks like more work | No tests and no error handling to show | The Demo anti-pattern |
| Generate the write-up | About 15 minutes | The document you are questioned on is not in your words | Detectable, and it defeats the walk-through |
| Avoid AI where it is encouraged | Costs implementation time | Less time left for verifying | Not a virtue here. Use it, then own it. |
The rule from the page, priced: AI is cheapest where its output is easiest to check, which is implementation and test cases. It is most expensive where its output becomes your judgement, which is scope, framing and the write-up.
Check yourself
In the walk-through the engineer asks why your cache stores 500 responses. You had not noticed it does. What do you say?
Own it. Say that it should not, why (one failure gets replayed to every user until the TTL expires), what the fix is, and why your tests missed it: none of them covered the error path. That is still a good answer. “The AI wrote that part” is the one answer that fails, because it is the Autopilot anti-pattern said out loud.
In the codebase review the code is long and unfamiliar. Can you paste it into an assistant for a summary?
No. Distyl’s post says this stage deliberately excludes AI, even though its careers page says AI is encouraged “at every stage”. When two sources disagree, ask the recruiter for that specific round. Meanwhile use a fixed checklist: correctness, failure paths, naming, tests, security, operability.
Live coding gives you an assistant that “won’t solve the problem for you.” Two candidates reach the same correct answer. What separates them?
What the interviewer saw along the way: how each prompt was framed, whether its output was checked before it was used, and how each wrong answer was spotted and corrected. Narrate every accept and reject. A silent candidate who pastes a working answer leaves them nothing to score.
The systems round asks how you would build a claims-intake assistant for an insurer from scratch. What should the first answer cover?
A compound system, not a model call. Name the parts: retrieval over policy documents, tools to read the claims system, an evaluator, and a human fallback for low confidence. Then say how you would measure it in production and who owns that number, since Distyl’s AI Engineers answer for production outcomes. If you are unsure of something, “here is how I would find out” beats guessing.
Quick answers
Does Distyl allow AI in interviews?
Yes — Distyl has published that AI assistance is encouraged in its take-home, because it reflects how the work is actually done. The evaluation shifts to judgement, verification and defence: you are expected to explain every decision in the artefact, including which AI-generated options you rejected and why.
What are the anti-patterns Distyl rejects?
Three. Autopilot — shipping generated code you cannot explain. Default — accepting the model’s first framing of the problem instead of scoping it yourself. Demo — building an impressive-looking breadth of features with no error handling or tests instead of one complete working path.
How should I use AI in a take-home interview?
Keep scoping, verification and the write-up entirely human; use AI freely for generating implementation and test cases, and for producing design options you then reject with reasons. Include a short “how I used AI” section in the write-up covering what you generated, what you rejected and what you verified by hand.
Is it bad to admit using AI in an interview?
Not where it is permitted, and increasingly not anywhere. When AI use is expected, transparency reads as judgement and pre-empts the hardest defence question. What fails candidates is not the use of AI but the inability to defend the result — saying “the AI wrote that part” as an explanation is the weakest possible answer, because the artefact is yours.
Sources
Every number on this page traces to one of these. Where a figure is self-reported or crowd-sourced rather than first-party, it is labelled inline.
Cited on this page
- Distyl AI — “How we do technical interviews at Distyl” (first-party) — distyl.ai/blog/engineering/how-we-do-technical-interviews-at-distyl
- Distyl AI — “Interviewing with AI at Distyl” (first-party) — AI-assisted take-home and the anti-patterns they reject. distyl.ai/blog/frontier/interviewing-with-ai-at-distyl
- Latent Space — Forward deployed engineers (AI Engineer World’s Fair) — Ramp’s shift toward engineers supervising agent harnesses. www.latent.space/p/forward-deployed-engineers-aiewf
- Distyl AI — Careers: “How we hire” (first-party) — interviews over Google Meet; AI encouraged; 3 office days a week in SF or NYC for engineering. www.distyl.ai/careers
- Distyl AI — AI Engineer (job posting, first-party) — compound AI workflows, evaluation, 10–30% travel. jobs.ashbyhq.com/Distyl/46728319-c47a-48c8-bbfb-af0d9bff2ea2
- Distyl AI — Case studies (first-party) — company-reported outcomes; anonymised customers. www.distyl.ai/case-studies
- Crunchbase News — Distyl raises $175M Series B at $1.8B valuation (22 Sep 2025) — founders Arjun Prakash and Derek Ho, both ex-Palantir. news.crunchbase.com/ai/distyl-raises-valuation-175m-seriesb/
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