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FDE Toolkit · Interview Cheat Sheet

The FDE interview loop, decoded

The FDE loop is not a standard SWE loop. It tests whether you can scope an ambiguous customer problem, design and build an AI solution, debug it live, and survive a security review — often in front of the customer. Here is every round, the framework to attack it, why strong engineers get rejected, the real company loops, and a six-week plan to be ready.

9 rounds 4 frameworks 6 company loops 48+ questions
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The loop — nine rounds

A full FDE loop runs 5–8 stages over 3–6 weeks. Not every company runs every round, but the shape is consistent — and the decomposition / case round is the through-line across the field. Click any round to jump to its breakdown.

  1. 01 Recruiter & HM Screens Gate
  2. 02 Practical Coding Core
  3. 03 AI System Design Core
  4. 04 Decomposition / Case Study FDE-specific
  5. 05 System-Design Debugging FDE-specific
  6. 06 Client Simulation FDE-specific
  7. 07 AI-Assisted Pair Coding Core
  8. 08 Behavioral & Values (STAR+) Core
  9. 09 Procurement / Security FDE-specific
Gate Core FDE-specific

Round by round

Each round, with what it tests, the framework to attack it, what gets you rejected, and sample problems.

01
Recruiter & HM Screens
The gate · Gate

Tests Two short calls. The recruiter tests motivation — and one question filters more candidates than any other: "Why FDE, not a regular SWE role?" The hiring manager then drills one or two past projects to confirm you actually owned the outcome, not just the code.

Attack it Have a crisp, personal answer for why customer-facing technical work — tie it to something you have actually done. In project stories, say "I" not "we": name your decisions, your trade-offs, your result.

Rejected for A generic "I want to work at a frontier lab" answer. Describing team work as "we did" so the interviewer cannot find your contribution.

Sample problems
  1. Why forward-deployed engineering specifically, and not a pure SWE or MLE role?
  2. Walk me through the most technically challenging project you owned end to end.
  3. Tell me about a deployment you took from scoping all the way to production.
  4. Why this company — name a customer, product, or piece of research that pulls you here.
02
Practical Coding
Not LeetCode-hard · Core

Tests FDE coding sits at a comfortable medium and is contextualized in a customer scenario, not an abstract puzzle. They watch whether you ask clarifying questions, write clean readable code, narrate as you go, catch your own bugs, and make pragmatic trade-offs.

Attack it Clarify the spec before typing. Narrate every decision. Favour robust, integration-grade code — retries, rate limits, caching, error handling — over exotic algorithms. Refresh core DSA to a solid medium, but do not grind 300 problems.

Rejected for Coding in silence. Optimizing an algorithm nobody asked about while ignoring messy-input edge cases the customer will actually hit.

Sample problems
  1. Parse a messy CSV with inconsistent quoting and missing fields into clean records.
  2. Implement a rate limiter supporting both per-user and global limits.
  3. Build a small RAG pipeline over a folder of documents.
  4. Implement retry-with-backoff-and-jitter and a circuit breaker for a flaky tool call.
  5. Write a SQL query to find customers with a 30%+ return rate, then make it fast.
03
AI System Design
Core · Core

Tests Design an AI solution for a specific customer with known constraints — VPC deployment, SSO, HIPAA/SOC 2, legacy integration — not for abstract "users at scale". You must decide agentic vs deterministic and defend latency, cost, reliability, and where a human stays in the loop.

Attack it Run the four stages: scope the customer's real constraints → decompose into sub-problems → propose a walking-skeleton MVP → make trade-offs explicit. Always address the FDE-specific layer standard prep skips: private deployment, identity (SAML/OIDC/SCIM), data residency, and evals.

Rejected for Jumping straight to a perfect production architecture instead of a walking skeleton. Designing in a vacuum without the customer's constraints.

Sample problems
  1. Design a private, VPC-deployed RAG system for a healthcare customer over 50M documents with HIPAA constraints.
  2. A customer wants a RAG assistant over 2M internal docs with strict access control. Design retrieval, permissions, and evaluation.
  3. Build an evaluation harness for a shipment-rerouting agent with a 99% accuracy target.
  4. The customer needs sub-2s latency at $0.05/query. Redesign your agent to hit both.
  5. Design versioning, A/B testing, and rollback for prompts in production.
04
Decomposition / Case Study
The FDE signature round · FDE-specific

Tests The single biggest filter in the FDE process — the Palantir-origin round the whole field inherited. You get a massive, vague, real-world problem and ~60 minutes. There is no code: they score how you break ambiguity into a phased, deployable plan.

Attack it The five-step decomposition: (1) clarify the problem and goals, (2) identify stakeholders and success metrics, (3) map available inputs and data, (4) decompose into sub-problems with sequencing rationale, (5) propose a walking-skeleton MVP, then iterate. "Slow is smooth, smooth is fast" — scope before you solve.

Rejected for Jumping to a solution before scoping — the most common rejection in the entire loop. Forgetting the end user. Never stating assumptions or trade-offs.

Sample problems
  1. A major city wants to reduce 911 response times. You have call, traffic, and ambulance-GPS data. 60 minutes. Go.
  2. A regional bank wants to "use AI to reduce fraud" across three acquired systems. Scope the first deployable slice.
  3. A logistics firm wants an agent to auto-reroute shipments. How do you build it — and how do you evaluate it?
  4. The customer's data is far messier than promised on day one. How do you re-sequence the engagement?
  5. The exec sponsor and the end users want different things. Decompose to satisfy the renewal and the users.
05
System-Design Debugging
FDE-specific · FDE-specific

Tests A distinctive round at enterprise-deployment shops: you get a complex architecture diagram and a deliberately vague prompt — "a customer reports requests are failing, debug it." No stack traces, no hints. It mirrors being on-call in a customer environment with incomplete information.

Attack it Run a hypothesis-driven investigation out loud: reason about failure domains, state your most-likely hypothesis and why, ask for the specific log / metric / trace that would confirm it, and pivot explicitly when the evidence contradicts you. Make the investigation legible — converge, don't thrash.

Rejected for Reciting a generic checklist ("check the LB, check the DB, check the cache") with no prioritization. Force-fitting evidence to your first guess instead of updating.

Sample problems
  1. Customer reports intermittent request failures across this distributed system. Find the root cause.
  2. A third-party API is timing out intermittently in production. Walk me through your diagnosis.
  3. Diagnose high latency in an LLM inference pipeline — is it generation, network, preprocessing, or retrieval?
  4. Model quality has silently degraded over two weeks. How do you investigate drift?
  5. A deployment fails at 2 a.m. Walk me through your incident response, minute by minute.
06
Client Simulation
FDE-specific · FDE-specific

Tests A live role-play: an interviewer plays a customer — often a non-technical executive — and you must navigate a hard conversation. Half the FDE job is translating between engineering and the business, and this round tests it directly. Many strong engineers treat it as fluff and fail.

Attack it Acknowledge the customer's position as valid before you push back. Ask diagnostic questions before proposing. Use ownership language. Never overpromise — be honest about limits (an LLM cannot guarantee 100% accuracy) without losing the room.

Rejected for Over-promising to keep the customer happy. Going straight to a solution before understanding the concern. Hiding behind jargon with a non-technical stakeholder.

Sample problems
  1. The deployment slipped three weeks and the customer's CTO is on the line. Tell them.
  2. The customer wants a feature that would compromise data governance. Push back.
  3. Explain to a non-technical VP why your RAG system cannot guarantee 100% accuracy.
  4. The customer's security team will not provide production credentials. Resolve it.
  5. You disagree with the customer's chosen architecture. Hold the line without breaking the relationship.
07
AI-Assisted Pair Coding
Increasingly core · Core

Tests Build live with an AI coding assistant (e.g. Claude Code). They watch how you direct the model, what you accept vs reject, and whether you ship something that actually works. Agentic coding tools are now named in a large share of senior FDE JDs.

Attack it Treat the model as a pair, not an oracle: state intent, review its output critically, reject confidently, and keep ownership of the design. Narrate why you accept or reject each suggestion. Ship something working in the time given.

Rejected for Blindly accepting generated code you cannot defend. Or refusing to use the tool and hand-rolling everything slowly.

Sample problems
  1. Using an AI assistant, build a working tool-calling agent against this mock API in 45 minutes.
  2. Debug this failing multi-agent workflow with the assistant; narrate your process.
  3. Add a guardrail and PII-redaction layer to this agent, AI-assisted.
  4. Refactor this prototype into a deployable service — explain what you accept and reject from the model.
  5. Write an eval harness for this agent's output quality, pair-programming with the model.
08
Behavioral & Values (STAR+)
Embedded throughout · Core

Tests FDE behavioural is woven through every technical round, not confined to one — at some shops ~20 minutes of every round. It tests a specific brand of ownership ("a deployment fails at 2 a.m.; you don't file a ticket, you fix it") plus company-specific values alignment that can reject a technically strong candidate.

Attack it STAR+: Situation, Task, Action, Result — plus the customer impact and the ownership. "I cut query time 40%" is a SWE answer; "...which let analysts finish daily reports in minutes, tripling their capacity" is an FDE answer. Keep each story 60–90 seconds. Read the company's charter / safety research and have a genuine "why".

Rejected for Surface-level motivation and rehearsed talking points. Technical stories with no customer or business dimension. Values misalignment — fatal even for strong coders.

Sample problems
  1. Tell me about a time you shipped under ambiguity with a demanding customer.
  2. Describe a deployment that went badly in production. What did you do?
  3. A customer asked for something technically unwise. How did you hold the line?
  4. Tell me about driving alignment on a team you did not manage.
  5. Tell me about spotting a pattern across customers and changing the product or workflow.
09
Procurement / Security
FDE-specific · FDE-specific

Tests The enterprise-buyer simulation — security review, compliance, data handling, and defending a statement of work. Where many strong engineers are caught off guard, because generic prep ignores it entirely.

Attack it Know SOC 2 / HIPAA / FedRAMP / GDPR at a working level. Be able to scope access and audit for an agent acting on customer systems, and to defend pricing and scope to a procurement officer without folding.

Rejected for Treating compliance as someone else's job. Caving on scope or price the moment procurement pushes.

Sample problems
  1. The customer's security team blocks your deployment one week before go-live. Walk me through it.
  2. Explain your data-handling for a HIPAA-regulated deployment.
  3. The procurement officer pushes back on your SoW pricing. Defend it.
  4. How do you scope access and audit for an agent acting on customer systems?
  5. Walk through SOC 2 / FedRAMP considerations for an embedded deployment.

Several rounds turn on the projects you've shipped. Build an FDE-ready portfolio →

The four frameworks that win the room

Most rounds are won or lost on process, not trivia. These are the four repeatable structures interviewers are scoring you against. Internalize them and you stop improvising under pressure.

Case study · open-ended round

Decomposition — the 5 steps

  1. Clarify the problem and the real goal
  2. Identify stakeholders and success metrics
  3. Map the available inputs and data sources
  4. Decompose into sub-problems — and justify the sequence
  5. Propose a walking-skeleton MVP, then iterate
System-design round

AI System Design — the 4 stages

  1. Scope the customer's actual constraints (VPC, SSO, compliance)
  2. Decompose into sub-problems
  3. Propose an MVP that shows iterative thinking
  4. Make trade-offs explicit — and add evals + data residency
Behavioural · values

STAR+ for FDE

  1. Situation and Task — set the stakes fast
  2. Action — your decisions, in the first person
  3. Result — the metric
  4. + Customer impact — what it meant for the customer
  5. + Ownership — keep it to 60–90 seconds
System-design debugging

Hypothesis-driven debugging

  1. Reason about failure domains, not a checklist
  2. State your most-likely hypothesis and why
  3. Ask for the one signal that would confirm it
  4. Pivot explicitly when evidence contradicts you
  5. Converge — make the investigation legible

Why strong engineers fail

Technical talent is necessary, not sufficient. These are the ten patterns that reject otherwise-strong candidates — most of them have nothing to do with whether you can code.

  1. 1Treating it like a standard SWE interview — grinding LeetCode for a loop that barely tests it.
  2. 2Jumping to a solution in the decomposition round before scoping — the single most common rejection.
  3. 3A generic "why this company?" with no specific customer, product, or research named.
  4. 4Saying "we" instead of "I" — the interviewer cannot find your actual contribution.
  5. 5Under-preparing the client simulation, treating role-play as fluff.
  6. 6Coding and designing in silence — interviewers cannot score what you do not say.
  7. 7Hand-waving on evaluation: "it looks right" instead of how you actually measure quality.
  8. 8Over-promising in the role-play to keep the customer happy.
  9. 9Technical stories with no customer or business dimension.
  10. 10Values misalignment — fatal even for technically excellent candidates.

Most failures trace back to a gap you can close before the loop. Find your gap on the FDE scorecard →

How real companies run it

The shape is shared, but every company tilts it. Below, the documented loops — flagged by how much we can evidence — so you prepare on facts, not folklore. Deeper role breakdowns link to our lab deep-dives.

Palantir (FDSE) Known pattern
Recruiter Karat coding Coding System design Open-ended decomposition Behavioural HM final

Origin of the decomposition round the whole field inherited. ~28 days, 5–6 rounds, with ~20 min of behavioural embedded in every technical round. Widely considered among the hardest loops in tech because of the unfamiliar open-ended round.

OpenAI Documented
Recruiter Take-home (~5 hrs, build on OpenAI APIs) Video walkthrough Take-home deep-dive Onsite: HM · solution design · technical

The take-home plus recorded video walkthrough is the most distinctive element — a direct simulation of presenting to a customer. The technical deep-dive pushes hard on evals: "how do you know your AI system is actually working?" ~3 weeks. FDE pairs with a Forward-Deployed SWE.

Anthropic (Applied AI) Documented
Recruiter Take-home HM screen Skills coding (~90 min) Technical Behavioural / mission

A founding Applied-AI seat at a ~3+ YOE bar. Practical coding, not LeetCode, with a progressive assessment. Mission alignment is screened seriously — be ready to discuss Constitutional AI and the Responsible Scaling Policy. Reportedly firm on offers.

Databricks Known pattern
Recruiter Case round AI system design Platform-specific build

The most built-out FDE org; billable delivery. Expect Spark, SQL, data modeling, MLflow, lakehouse architecture, and RAG over enterprise datasets alongside the case and system-design rounds.

Apple Documented
5–6 rounds across 6 evaluation dimensions

The one fully documented loop in our archive — recruiter-shared for the AI Evaluation Platform seat. Roughly half the bar is eval-domain craft + hands-on integration; the other half is partnership, influence, and adoption. A strong coder who cannot drive internal adoption fails here.

Cohere Documented
Hiring manager System-design debugging Architecture presentation VP behavioural HR

No LeetCode at all. The system-design debugging round (debug a failing distributed system under ambiguity) is the one to prepare most. The VP round wants the loop from recurring customer pain to a durable product fix — not one-off workarounds.

Also building FDE / FDE-adjacent loops: GoogleMetaSalesforceGleanScale AIRampSierraPostmanStripe

The six-week prep countdown

A focused plan beats months of unfocused grinding. Here is how to spend the six weeks before an FDE onsite.

  1. Wk 1
    Foundation

    Audit your résumé for ownership language and measurable outcomes. Draft your "why FDE / why this company" answers. Read each target's engineering blog and recent launches.

  2. Wk 2
    Coding

    Five practical exercises — rate limiter, CSV parser, streaming consumer, retry/backoff, small RAG pipeline — plus SQL with window functions. Practice narrating as you code.

  3. Wk 3
    System design

    Four enterprise problems covering data flow, trust boundaries, auth, observability, failure modes, and rollback. Always start with a walking skeleton.

  4. Wk 4
    Decomposition

    Timed 60-minute case sessions across healthcare, finance, logistics, retail, public-sector. Self-record and check for jumping-to-solutions.

  5. Wk 5
    Behavioural & simulation

    Write 8 STAR+ stories, each 60–90 seconds spoken. Role-play client simulations with a partner; drill ownership language and de-escalation.

  6. Wk 6
    Company-specific & mocks

    Run two full mock loops. Refine company-specific motivation. Re-read target research and launches. Rest 48 hours before the onsite.

Why IK

IK prep includes FDE-tuned mock interviews with FAANG+ engineers, plus resume and LinkedIn support to round out the loop.

The question bank · 48+ problems

Every category, drawn from reported FDE loops across the field. Open a category and drill it.

Coding & engineering

  1. Rate limiter supporting per-user and global limits.
  2. Parse a messy CSV with inconsistent quoting.
  3. Build a CLI tool that ingests PDFs into a JSON index.
  4. Streaming consumer that handles backpressure.
  5. Refactor a 200-line function for testability.
  6. Exponential backoff with jitter.
  7. Small RAG pipeline over a document set.
  8. Top-k similar items in a 10M-vector index.
  9. SQL: customers with a 30%+ return rate; then optimize it.
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