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The FDE interview,
round by round
Roughly half of an FDE loop is not algorithms — it is scenario work that generic interview prep does not touch. Nine archetypes cover almost every round any company runs. Here is what each one is testing, what interviewers actually score, and where to practise it rather than read about it.
Why generic prep fails here
A standard software loop optimises for can you produce a correct solution. An FDE loop optimises for can we send you to a customer alone. So the rounds test scope-setting before solving, learning speed on unfamiliar material, judgement under incomplete information, and how you behave when something breaks in front of someone.
Candidates who grind algorithm problems and then fail an FDE loop almost always fail on the same thing: they started solving before they scoped.
Every published guide describes these rounds and then ends with the same advice: find a human partner and role-play. That is the gap. Each archetype below links to a graded simulation you can run right now.
Interview details are labelled by source type, per the standard used throughout this track: first-party (company blog or job description) > named first-hand (a person writing under their name) > aggregated / anonymous (Blind, Glassdoor, guide sites). Loops change; verify against your recruiter’s description.
The nine archetypes
Learn these and you can walk into any FDE loop and recognise what is happening in the first two minutes.
Decomposition — the signature FDE round
An open, non-coding problem: “design a system to help a city’s taxi fleet”, “improve 911 response times”, “unify fraud signals across five systems”. You are scored on the clarifying-question phase before you propose anything, then on a structured, prioritised approach.aggregated Palantir’s version is the best-known.1920
Scored on: scoping before solving, structure, prioritisation, naming assumptions, knowing what you would not build. Fails on: jumping to architecture in minute two.
Learning round
You are handed unfamiliar documented material — a custom library, an internal API, an odd data format — and asked to absorb it and use it under time pressure. Reported Palantir versions have used a bespoke package-installer library with concurrency semantics.aggregated19
Scored on: reading strategy, asking the right question at the right time, admitting what you have not understood yet, correct use rather than memorised recall. Fails on: bluffing.
Incremental coding
A small problem that grows: each stage adds a constraint that invalidates the shape of your previous solution — a cache that becomes an LRU, then bounded, then thread-safe. Sierra runs a live incremental CoderPad round; Anthropic’s reported CodeSignal stage escalates similarly.aggregated2422
Scored on: whether your first solution was over- or under-engineered, and how gracefully you refactor. Fails on: a clever one-liner you cannot extend.
Debugging & re-engineering
Unfamiliar code that is wrong. Sometimes explicitly planted bugs — Sierra’s onsite has been reported as a debugging round with three planted defects.named first-hand24 Palantir’s re-engineering round hands you a working-but-bad implementation to improve.19
Scored on: systematic narrowing rather than guessing, reading before editing, verifying the fix. Fails on: shotgun changes with no hypothesis.
Take-home + defence
Multi-hour realistic build, then a live or recorded walkthrough. OpenAI’s reported process centres on an API-heavy take-home of roughly five hours with a required video walkthrough.named first-hand21 Sierra’s has been a support agent built without an agent framework.24 Distyl explicitly encourages AI assistance and interrogates how you used it.first-party27
Scored on: scope judgement, what you cut, how you tested, and whether you can defend every decision. Fails on: a beautiful artefact you cannot explain.
Client roleplay / case
An interviewer plays a customer: a demo fails live, a stakeholder demands the wrong feature, security blocks your data access, a middle manager is quietly hostile. OpenAI’s loop is reported to weight case, empathy and business judgement at roughly half the evaluation.named first-hand21
Scored on: listening before answering, honest handling of bad news, saying no without losing the room. Fails on: defensiveness, or over-promising to be liked.
Values / culture
Not a formality. Anthropic’s values round is reported as the highest-failure stage of its Applied AI loop.aggregated23 The questions probe how you behave when the honest answer is inconvenient.
Scored on: specificity, consistency with your other answers, evidence you have actually changed your mind about something. Fails on: rehearsed idealism with no example attached.
System design, customer-flavoured
Not “design Twitter”. “Design ingestion for a customer sending 200k webhooks a day with a two-hour outage window”, or “design multi-tenant isolation for a bank that will not share a database”.
Scored on: constraints first, failure modes, operability by their team, and cost. Fails on: a whiteboard architecture nobody could run.
Behavioural ownership
“Tell me about a time you shipped something a customer did not adopt.” “When did you tell a customer no?” “What did you get wrong in a deployment?”
Scored on: saying “I” rather than “we”, a measurable outcome, and a real failure with a real lesson. Fails on: a team story where your role is invisible.
The five signals every company screens for
Across every published loop, the same handful of behaviours decides outcomes. Optimise these and the specific round matters less.
| Signal | What it looks like when present | What kills it |
|---|---|---|
| Scope before solve | Your first three minutes are questions and stated assumptions, not architecture. | Proposing a solution before you know what success means. |
| Narrate always | Continuous, calm commentary on what you are doing and why — especially while stuck. | Silence. An interviewer cannot score thinking they cannot hear. |
| “I”, not “we” | Specific personal decisions, with what you would do differently. | Team narratives where your contribution is unclear. |
| Eval rigour | You ask how correctness will be measured, and propose a way to test it. | Treating “it works” as self-evident. |
| Production over algorithms | Error handling, idempotency, what happens on retry, what you would monitor. | Optimal complexity with no thought about failure. |
Open every technical round with: “Before I start — what does success look like here, what can I assume about scale and data quality, and what is explicitly out of scope?” It costs 30 seconds and it directly demonstrates three of the five signals.
Company loops
The same nine archetypes, combined differently. Each page carries the reported stage list, the signature round, and how to prepare specifically.
| Company | Shape | Signature round | Guide |
|---|---|---|---|
| Palantir (FDSE + Deployment Strategist) | Online assessment, then a panel drawn from a pool of round types | Decomposition — the most imitated round in the industry | Palantir loop → |
| OpenAI (FDE) | Multi-stage with a substantial take-home and a required walkthrough | Take-home defence, plus heavy case/empathy weighting | OpenAI loop → |
| Anthropic (Applied AI / FDE) | Five reported stages including a timed coding assessment | Values round — reported as the highest-failure stage | Anthropic loop → |
| Sierra (Agent Engineer) | Live incremental coding, agent take-home, debugging onsite | Debugging with planted bugs | Sierra loop → |
| Distyl AI | AI-assisted take-home plus a defence conversation | How you used AI — and which anti-patterns you avoided | Distyl loop → |
Other companies hiring into this shape — Databricks, Glean, Scale, Cohere, Mistral, Baseten, vertical AI startups4 — recombine the same archetypes. If you can run all nine, you are prepared for a loop that has not been documented yet.
A four-week prep plan
| Week | Focus | Concretely |
|---|---|---|
| 1 | Scoping reflex | Run the decomposition simulator on all four cases. Practise the opening question out loud until it is automatic. Read Discovery & Scoping. |
| 2 | Code under FDE conditions | Incremental coding, then the three re-engineering challenges. Time-box each; narrate aloud while solving, even alone. |
| 3 | The customer half | Client roleplay across all four scenarios, learning round, and one customer-flavoured system design. |
| 4 | Stories, take-home, loop specifics | Write five ownership stories with numbers. Do a timed take-home and record the defence. Read your target company’s loop page. |
Every round, with a drill attached
These are the interactive simulators for the rounds every other guide tells you to rehearse with a friend. Graded, free, no signup.
Questions to ask them
The reverse interview matters more in this role than any other, because the five career costs are properties of the specific job.
Detects the real job
- What percentage of my time is expected to be writing code?
- Show me something an FDE built last quarter that is now in the product.
- Is any part of my review tied to utilisation or billable hours?
- How many customers will I have at once?
Detects how it will feel
- What is the travel expectation, in writing?
- How does an engagement end, and who is on-call after handoff?
- Who defines success for a deployment, and is it written down first?
- What happened to the last person in this seat?
These are not filler at the end of the call — asking them well is itself a signal, because it is exactly the behaviour the job requires: scoping before committing. Why each question matters →
The picture: the first minutes of a decomposition round
Worked example: the 200k-webhooks design round, step by step
Take the customer-flavoured prompt from archetype 8: a customer sends 200,000 webhooks a day and has a two-hour outage window. Constraints come first, so do the arithmetic out loud before you draw a box. The 5× peak, the 4× drain capacity and the 2 KB payload are assumptions you state, not facts you were given.
| Step | Question | Arithmetic | Answer |
|---|---|---|---|
| 1 | Average rate | 200,000 ÷ 86,400 s | ≈ 2.3 webhooks/s |
| 2 | Assumed peak (say 5× average) | 2.31 × 5 | ≈ 11.6/s: small for any queue |
| 3 | Backlog after a two-hour outage | 200,000 × 2 ÷ 24 | ≈ 16,667 webhooks |
| 4 | Backlog size at an assumed 2 KB each | 16,667 × 2 KB | ≈ 33 MB: storage is not the problem |
| 5 | Drain time at 4× average capacity | new traffic keeps arriving: 16,667 ÷ (9.26 − 2.31)/s | 2,400 s = 40 minutes |
| 6 | What breaks on replay | senders retry during the outage, then you replay the backlog | Duplicates: dedupe on an event id |
| 7 | Who runs it | their team, after you leave | One queue, one dead-letter queue, one lag alarm |
Steps 1 to 4 take a minute and clear away the scale worry: the volume is small. That frees the rest of the round for what actually fails here. Step 5 shows the backlog drains with new traffic still arriving, so recovery is 40 minutes, not instant. Say that number to the customer before they find out. Step 6 is the production-over-algorithms signal: replay means every write must be idempotent. Step 7 is operability, which is scored too. A design nobody on their team can run fails the round, however clean it looks on the whiteboard.
Cost and trade-offs: where four weeks of prep go
For prep, Time is the week of the plan above, Space is the signal that week trains, and Why is what that week buys you in the loop.
| Case | Time | Space | Why |
|---|---|---|---|
| Decomposition + scoping | Week 1 | Scope before solve | The behaviour that fails the most candidates, and the signature round at Palantir. Cheapest to fix, because it is one habit. |
| Incremental coding + re-engineering | Week 2 | Production over algorithms, narrating | Each stage breaks the last design, so it rewards code that is simple and easy to extend, not clever. |
| Roleplay, learning round, customer system design | Week 3 | Listening, honesty, constraints first | The half of the loop that generic prep never touches, so most candidates arrive with no practice at all. |
| Stories, take-home, company loop | Week 4 | Saying “I”, eval rigour | Stories with numbers can be written once and reused. Recording a take-home defence shows you gaps you cannot hear while you talk. |
| Extra algorithm grinding | Any week | Algorithmic correctness only | It helps only the coding part of the loop, and the FDE algorithm bar is often lower than at product companies. Past that bar, more grinding adds little. |
The plan front-loads scoping because it is used in every round. A good opening question improves decomposition, system design, the take-home memo and the roleplay. Extra algorithm practice improves only part of one round type.
Common mistakes in FDE prep
- Clarifying questions as a checklist
Asking ten questions and then giving the design you had already planned scores worse than asking three. Every answer should visibly change something: a constraint, a priority, a cut. If it changes nothing, say why it doesn’t.
- Asking when you should assume
When the interviewer says “you decide”, they want an assumption, not another question. Say it out loud (“I’ll assume the fleet already has GPS data”), say why, and move on. Waiting for permission reads as an FDE who cannot work alone at a customer.
- Over-building stage one
In incremental coding, a stage-one design that tries to cover every later stage uses up the hour and still guesses the wrong next constraint. Write the simplest version that can be extended, say what it can’t do yet, and refactor when the next stage arrives.
- One story for every behavioural question
Values answers are checked against your other answers. If you say you always push back on customers and later describe quietly shipping something you disagreed with, the interviewer will notice. Prepare five different stories so you never have to stretch one to fit.
- Preparing only the documented rounds
Some panels are drawn from a pool of round types, and loops change. A company page tells you what is likely, not what you will get. Drill all nine archetypes and you are ready even when the loop has changed since the last candidate wrote about it.
Check yourself
You get the 200k-webhooks prompt. What do you say in the first minute, before drawing anything?
The opener: what success means (no lost events? a recovery time?), what you can assume about payloads and peaks, and what is out of scope. Then the quick arithmetic: about 2.3/s on average and about 16,667 events after a two-hour outage. That shows the scale is small, and the round turns to replay and duplicates, which is the real problem.
Your stage-one cache is a clever one-liner. Stage three asks for it bounded and thread-safe. What happens?
You rewrite it from scratch while the clock runs, which is exactly the failure the round is built to catch. A plain class with a dict and a method per operation can take an eviction order and a lock without changing shape. The question is never whether the first version was short. It is whether it could grow.
In a client roleplay your demo fails live. Which reply scores: explaining that it worked this morning, or something else?
Something else. Say what failed in plain words, what you will check, and when you will come back, and then keep the meeting useful with what you can still show. Defending yourself and over-promising are the two named ways to fail this round. Being honest about bad news is what is being scored.
You are stuck on a debugging problem and go quiet for two minutes while you think. What did that cost?
Two minutes in which nothing could be scored. The interviewer cannot see your thinking, only hear it. Say your current hypothesis and what would rule it out, even if it turns out wrong. A wrong hypothesis you test out loud shows the systematic narrowing the round is looking for. Silence shows nothing.
Quick answers
What is the forward deployed engineer interview process?
It varies by company but recombines nine round archetypes: decomposition, a learning round, incremental coding, debugging or re-engineering, a take-home with a defence, client roleplay or case, values, customer-flavoured system design, and behavioural ownership. Roughly half of a typical loop is scenario work rather than algorithm coding, which is why generic interview preparation transfers poorly.
What is the decomposition round in an FDE interview?
An open, non-coding problem — for example designing a system to help a city’s taxi fleet or improve emergency response times. You are scored primarily on the clarifying-question phase before you propose anything, then on a structured and prioritised approach. It is Palantir’s signature round and the most imitated round in FDE hiring. Jumping to architecture in the first two minutes is the most common way to fail it.
How do I prepare for a forward deployed engineer interview?
Four weeks works well: week one on the scoping reflex and decomposition practice, week two on incremental coding and debugging unfamiliar code, week three on the customer half — client roleplay, learning round, customer-flavoured system design — and week four on ownership stories with numbers, a timed take-home with a recorded defence, and your target company’s specific loop.
Is the FDE interview harder than a normal software engineering interview?
Different rather than uniformly harder. The algorithm bar is often lower than at a large product company, but the loop tests scoping under ambiguity, learning speed on unfamiliar material, live debugging, customer communication and values — dimensions most engineers have never practised. Hiring guides describe the target profile as staff-engineer judgement, so the seniority expectation is high.
What do FDE interviewers actually score?
Five signals recur across every published loop: scoping before solving, narrating your thinking continuously (especially when stuck), saying “I” rather than “we” in behavioural answers, asking how correctness will be measured, and favouring production judgement — error handling, idempotency, monitoring — over algorithmic optimality.
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
- Jobs by Culture — FDE hiring boom 2026 — per-company openings snapshot. jobsbyculture.com/blog/forward-deployed-engineer-boom-2026
- First Round Review — “So you want to hire a forward deployed engineer” — the staff-engineer bar. review.firstround.com/so-you-want-to-hire-a-forward-deployed-engineer/
- Exponent — Palantir Forward Deployed Engineer interview guide — www.tryexponent.com/guides/palantir-forward-deployed-engineer-interview
- Blind — Palantir FDSE interview, decomposition round (candidate account) — anonymous first-hand report; lower confidence than a first-party source. www.teamblind.com/post/advice-wanted-palantir-fdse-interview-decomp-round-1ffoysoz
- Gaijineer — OpenAI Forward Deployed Engineer interview process — named first-hand candidate account. gaijineer.co/openai-forward-deployed-engineer-interview-process
- Blind — Anthropic Solutions Architect / FDE CodeSignal round (candidate account) — anonymous first-hand report. www.teamblind.com/post/anthropic-solutions-architectforward-deployed-engineer-codesignal-yhs7u5vt
- Perspective — Anthropic Applied AI Engineer interview process — aggregated candidate reports. www.getperspective.ai/blog/anthropic-applied-ai-engineer-interview-process-frontier-lab-2026
- Gaijineer — Sierra software engineer (agent) interview experience — named first-hand candidate account. gaijineer.co/sierra-software-engineer-agent-interview-experience
- Exponent — Sierra Agent Engineer interview guide — www.tryexponent.com/guides/sierra-agent-engineer-interview
- 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
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