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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 51+ 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

How much coding — and the DSA question

The most confused signal in FDE prep. FDE coding is practical, but it is not soft — and how much of it you face depends on the level you are targeting.

LevelCoding loadWhat it means
Junior 3–4 coding · 1 system design A light phone-screen coding round up front, then a coding-heavy loop. DSA carries the most weight here.
Senior / Staff 1–2 coding · design-weighted Fewer coding rounds, more system design — but coding never fully drops off. Even senior-staff FDE loops still run a round or two of it.
Principal / Director Varies — IC, management, or both Some seats are people-management only; others expect hands-on and management. A few regions (some Taiwan-based companies) push coding all the way to director level.

The DSA signal is genuinely mixed — which is why it confuses people. Tier-1 companies almost certainly still ask it. AI-native startups filter it thoroughly, at medium-to-high difficulty. Salesforce confirms it runs no DSA round at all. Read the target before deciding how hard to prep: sources calling FDE coding "low-to-medium" are underselling the AI-startup end of the market.

Round by round

Each round: what it tests, how to attack it, what gets you rejected, and the real questions. 51 of them, phrased the way an interviewer actually asks, each with what they are listening for. Open a round to drill it.

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 questions · 4
  1. Before we get into the technical loop, I want to understand the motivation. You've got a strong SWE background — so why forward-deployed engineering specifically, and not a pure SWE or MLE role? What pulls you toward the customer-facing side? Listening for: A crisp, personal, non-generic reason tied to real experience — genuine pull toward customer-facing technical delivery, not just 'I want to work at a frontier lab'.
  2. Pick the most technically challenging project you owned end to end and walk me through it — I want the scope, the hard decisions that were yours, the trade-offs you made, and how it landed. Talk in terms of what you did, not what the team did. Listening for: First-person ownership — the candidate can isolate their own decisions and trade-offs; measurable outcome; no hiding behind 'we'.
  3. Now tell me about a customer-facing engagement specifically — one where you had to manage stakeholders, not just code. Who were they, where did their goals conflict, and what did you hand over at the end? Listening for: Stakeholder management and handover discipline — the customer half of the job, distinct from raw end-to-end delivery.
  4. Why us specifically? Name a customer, a product, or a piece of research of ours that makes you want this seat over the other labs hiring FDEs right now. Listening for: Specific, researched motivation naming a real product/customer/research direction — not interchangeable 'frontier lab' enthusiasm.
02
Practical Coding
Practical, not soft · Core

Tests FDE coding is contextualized in a customer scenario rather than an abstract puzzle — but do not read that as easy. The bar runs medium-to-high, and it climbs at AI-native startups, which still filter DSA thoroughly even where some tier-1 companies have eased theirs. 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-to-high — heavier if you are targeting AI startups — but you do not need to 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. Walking in assuming FDE coding is a soft bar and skipping DSA prep entirely.

Sample questions · 7
  1. A customer hands you an export of their order data as a CSV, and it's a mess — inconsistent quoting, some fields missing, stray delimiters inside quoted values. Parse it into clean, typed records. Talk me through how you handle the rows that don't conform. Listening for: Clarifying questions before coding; clean readable code; deliberate handling of malformed real-world input rather than assuming a well-formed file.
  2. You're calling a customer's flaky internal API from your service and it fails intermittently. Implement retry-with-exponential-backoff-and-jitter, and put a circuit breaker in front of it. Explain why jitter matters and how you pick the breaker thresholds. Listening for: Integration-grade resilience code; understands why jitter prevents thundering-herd retries; sensible open/half-open/closed state design over clever algorithms.
  3. We need to protect a shared backend endpoint. Implement a rate limiter that enforces both a per-user quota and a global ceiling at the same time. Walk me through your data structure and what happens at the limit. Listening for: Correct handling of two simultaneous limits; sane choice of algorithm (token bucket / sliding window) and reasoning about the eviction/reset behavior and concurrency.
  4. Here's a customer database. Write a SQL query that finds every customer with a return rate of 30% or higher over the last year. Once it's correct, assume the orders table is hundreds of millions of rows — how do you make it fast? Listening for: Correct aggregation/ratio logic first, then a real optimization story — indexing, pre-aggregation, or avoiding a full scan — not premature cleverness.
  5. Build a small CLI that walks a folder of PDFs and produces a single JSON index — text plus basic metadata per document — so it can be handed to a retrieval step later. Ship something that actually runs on messy input. Listening for: Pragmatic end-to-end tool that handles unreadable/corrupt files gracefully; clean structure; ships working over perfect.
  6. You're consuming a stream of events from a customer system that can burst faster than you can process. Write a consumer that handles backpressure without falling over or dropping data silently. What's your strategy when the buffer fills? Listening for: Understands backpressure (bounded buffers, pausing/acking, load-shedding vs blocking) and the durability trade-off; narrates the failure mode explicitly.
  7. Here's a 200-line function that does everything and has no tests. Refactor it so it's testable, and add the first few tests. Tell me what you're pulling apart and why. Listening for: Identifies seams and side effects, isolates pure logic, improves readability without changing behavior; pragmatic test coverage of the risky paths.
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 questions · 7
  1. A hospital network wants a clinical-knowledge assistant over roughly 50M internal documents, and legal will not let a single byte leave their environment. Design a private, VPC-deployed RAG system for them under HIPAA — walk me through retrieval, where the model runs, identity, and how you'd prove it's safe to turn on. Listening for: Scopes the real constraints first (private deployment, PHI handling, BAA, audit); addresses the FDE-specific layer — data residency, SSO/identity, evals — not just a generic RAG box diagram.
  2. Same shape, different customer: a RAG assistant over 2M internal docs where different employees are allowed to see different documents. Design retrieval, the permission model, and how you evaluate it — I especially want to hear how you stop the model from surfacing content a user isn't cleared for. Listening for: Document-level access control enforced at retrieval time (not just at the UI), permission-aware indexing, and an eval plan that tests for leakage — the access-control layer is the real test.
  3. You're standing up a deployment inside a Fortune 500's AWS VPC, wired to Okta for SSO and Snowflake as the data source. Walk me through the architecture end to end, and where the trust boundaries and failure points are. Listening for: Comfort with enterprise integration reality — SAML/OIDC, network isolation, service-to-service auth, and identifying the boundaries where things break or leak.
  4. A customer wants an LLM-powered search experience over their corpus, and product is quoting you 'sub-100-millisecond' as the target because that's their bar for normal search. How do you design for it, and what do you tell product about that number? Listening for: Recognizes that a generation path cannot hit sub-100ms; separates retrieval latency (ANN can hit tens of ms) from generation; reframes to a realistic sub-second/streamed target with caching or precompute — and manages the expectation honestly.
  5. Your agent is answering at 1.5 seconds and $0.05 a query, and the customer wants it faster and cheaper without losing quality. Redesign to move both — walk me through the levers and the trade-offs each one costs you. Listening for: Concrete latency/cost levers — model tiering/routing, caching, prompt trimming, retrieval reduction, batching — with explicit awareness of the quality trade-off each introduces.
  6. A logistics customer has an agent that reroutes shipments, and they ask you to 'guarantee 99% accuracy' before go-live. Design the evaluation harness — and tell me how you'd actually agree on what 'accuracy' means here and what threshold is defensible. Listening for: Decomposes 'accuracy' into task-appropriate metrics (routing quality, harmful-action rate, human-override rate), builds a labeled eval set, and reasons about a defensible threshold with a human-in-the-loop for the tail — instead of accepting '99%' at face value.
  7. You're shipping prompts to production and they'll change often. Design versioning, A/B testing, and rollback for prompts — how do you roll one back at 2am when a new prompt quietly tanks answer quality? Listening for: Treats prompts as versioned, evaluated artifacts; online eval/guardrail metrics to detect regressions; fast, safe rollback path — production discipline, not ad-hoc editing.
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 questions · 6
  1. A major city wants to cut 911 emergency response times. You have historical call records, live traffic data, and ambulance GPS. You've got 60 minutes and no code — take me from this vague mandate to a phased, deployable plan. Listening for: Scopes before solving — clarifies the real goal, names stakeholders and success metrics, maps the data, then sequences sub-problems into a walking-skeleton first slice; never jumps to a solution.
  2. A regional bank tells you they want to 'use AI to reduce fraud,' but the data lives in three different systems from three banks they acquired that don't talk to each other. What's the first slice you'd actually deploy, and why that one first? Listening for: Handles fragmented/heterogeneous data as the core constraint; picks a defensible thin first slice with sequencing rationale rather than boiling the ocean.
  3. A pharma company wants a research query assistant over their internal literature, but legal is worried about IP leakage and regulatory exposure. Decompose the engagement — where do the legal and IP constraints change what you build and in what order? Listening for: Treats compliance/IP as first-class design inputs that reshape sequencing; scopes a defensible early deployment inside the legal guardrails rather than bolting them on later.
  4. An insurer wants to summarize claims across a 30-million-record backlog using LLMs, under insurance regulation. Where do you start, how do you sequence it, and how do you keep a regulator comfortable with an LLM in the loop? Listening for: Realistic staging over a huge regulated corpus — sampling, human review on the tail, auditability — and a first slice that proves value without betting the compliance posture.
  5. Two weeks in, the customer's data turns out to be far messier and less complete than what they promised on day one, and it breaks your original plan. How do you re-sequence the engagement without blowing the timeline or the relationship? Listening for: Re-planning under a broken assumption — re-scoping to what the data can support, resetting expectations early, protecting delivery; poise rather than pushing forward on a dead plan.
  6. The executive who signed the contract wants a flashy capability, but the people who'll actually use the tool every day want something different and less glamorous. Decompose the work so you satisfy the renewal and the end users. Listening for: Explicitly separates sponsor goals from end-user needs, keeps the end user in view, and finds a sequence that serves adoption and the renewal — the classic FDE stakeholder split.
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 questions · 6
  1. Here's the architecture diagram for a customer's deployment. They tell you requests are 'failing intermittently' — that's all you get, no stack trace, no logs yet. Walk me through how you find the root cause. Tell me which signal you'd ask for first and why. Listening for: Hypothesis-driven investigation — reasons about failure domains, states a most-likely hypothesis with justification, asks for the one metric/log/trace that would confirm it; converges instead of reciting a checklist.
  2. A third-party API this system depends on is timing out intermittently in production. Diagnose it out loud — how do you tell whether it's them, the network, or something you're doing to them? Listening for: Isolates the failure domain (client vs network vs upstream), reasons about retries/timeouts/connection pools amplifying the problem, and names the evidence that would discriminate between causes.
  3. Users say your LLM feature 'feels slow.' The latency could be in preprocessing, retrieval, the network hop, or generation itself. Walk me through how you localize where the time is actually going before you touch anything. Listening for: Instruments and attributes latency per stage before optimizing; understands the relative cost profile of each stage; doesn't blindly assume the model is the bottleneck.
  4. Over the last two weeks, answer quality on a deployed system has quietly gotten worse — no alarms fired, no deploys went out. How do you investigate whether this is drift, and what would you look at first? Listening for: Distinguishes data drift / input distribution shift / upstream data changes / silent dependency updates; uses an eval set or golden queries to quantify degradation rather than eyeballing it.
  5. A deployment fails at 2am in the customer's environment and you're the one on call. Walk me through your incident response minute by minute — what you do first, how you communicate, and how you decide to roll back versus push a fix. Listening for: Ownership under pressure, sane triage/mitigate-before-diagnose instinct, stakeholder communication, and a clear rollback-vs-forward-fix decision — the 'you don't file a ticket, you fix it' reflex.
  6. That 2am incident is resolved. Now run the post-mortem with me. What happened, what's the actual root cause versus the trigger, and what changes so it can't recur? Listening for: Blameless structure, separates trigger from root cause, and produces durable systemic fixes (guardrails, monitoring, process) rather than a one-off patch.
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 questions · 5
  1. I'm the customer's CTO and I've just joined the call. Your team's deployment has slipped three weeks and I don't know it yet. Tell me. Go. Listening for: Leads with the bad news honestly, owns it, comes with a revised plan and options; manages the room without over-apologizing or overpromising to recover.
  2. I'm the customer and I really want a feature that, the way I've described it, would blow a hole in your data-governance setup. I'm pushing hard. Talk me out of it — or find me another way. Listening for: Acknowledges the underlying need as valid before pushing back, diagnoses what they're really after, and offers a compliant alternative — holds the line without alienating the customer.
  3. I'm a non-technical VP and I want you to promise me this RAG assistant will be right 100% of the time. Explain to me, without jargon, why you can't promise that — and why I should still trust it. Listening for: Translates a probabilistic-system limitation into plain business language, sets an honest expectation, and reframes trust around guardrails/human review — keeps the room while being truthful about limits.
  4. I run the customer's security team and I'm not giving your engineers production credentials — full stop. Your go-live depends on it. Work it out with me. Listening for: Diagnoses the security team's real concern, proposes least-privilege / scoped-access / supervised paths, and negotiates a workable middle ground instead of escalating or caving.
  5. I'm the customer's lead architect and I've already decided on an approach that I think is the wrong one for this problem. I own this system. Change my mind without making me defensive. Listening for: Disagrees with respect and evidence, asks diagnostic questions, uses ownership language, and preserves the relationship while genuinely holding a technical position.
07
AI-Assisted Pair Coding
Increasingly core · Core

Tests Build live with an AI coding assistant of your choice — Claude, Codex, Copilot. The usual format is a blank slate with a set of mandatory features plus a few optional ones, built from zero. They score prompt quality, whether you can navigate the generated code to find the bottleneck and fix the logic, and whether you ship something that actually works. Some shops — Meta among them — bake the assistant into the DSA round itself and score your prompts as part of the solution. 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 questions · 5
  1. Using the AI assistant in front of you, build a working tool-calling agent against this mock API in 45 minutes. As you go, tell me why you're accepting or rejecting each thing the model gives you. Listening for: Directs the model with clear intent, critically reviews and rejects bad output, keeps ownership of the design, and ships something that actually runs in the time given.
  2. This multi-agent workflow is failing and I want you to debug it with the assistant. Narrate how you're using the model to investigate — and where you decide not to trust it. Listening for: Uses the model as an investigative pair without outsourcing judgment; forms and tests hypotheses; catches confidently-wrong suggestions.
  3. This agent is going into a customer environment. Pair with the assistant to add a guardrail layer and PII redaction on its inputs and outputs. Defend the design choices you accept from the model. Listening for: Knows what real guardrails and PII handling require, can evaluate the model's proposal critically, and can defend the resulting design rather than pasting it in blind.
  4. Here's a working prototype. Pair with the assistant to refactor it into a deployable service — retries, config, logging, the works. Talk me through what you take from the model and what you throw away. Listening for: Turns prototype into integration-grade service; maintains architectural ownership; explicit accept/reject reasoning rather than wholesale acceptance.
  5. Pair with the model to write an eval harness that measures this agent's output quality. How do you make sure the harness the model helps you write is actually measuring the right thing? Listening for: Can specify what 'quality' means for this task, scrutinizes model-generated eval logic for blind spots, and doesn't confuse 'it produced an eval' with 'the eval is valid'.
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 questions · 6
  1. Tell me about a time you had to ship something real under heavy ambiguity with a demanding customer breathing down your neck. What was yours to decide, and how did it turn out for the customer? Listening for: STAR+ with the customer/business dimension — first-person ownership, a real result, and what it meant for the customer, kept to 60–90 seconds.
  2. Walk me through a deployment or launch that went badly in production. What did you actually do in the moment, and what did you change afterward? Listening for: Ownership of a failure without deflection; concrete recovery actions; a durable lesson applied — not a rehearsed 'my weakness is I care too much' answer.
  3. Tell me about a time a customer asked you for something technically unwise, and you had to hold the line. How did you keep the relationship while saying no? Listening for: Principled pushback with the customer relationship intact; judgment about when the technical call outranks the customer's ask.
  4. Describe a time you drove alignment across a team you didn't manage — no authority, competing agendas. How'd you actually move them? Listening for: Influence without authority — the core FDE muscle; specific tactics, not just 'I communicated well'.
  5. Tell me about a technical decision you made and later had to reverse. What did you miss the first time, and what did you take away? Listening for: Intellectual honesty, willingness to change course on evidence, and a genuine learning rather than a humblebrag.
  6. Say you get this role. Walk me through your first 30, 60, and 90 days — how you'd ramp, earn trust with your first customer, and start delivering. Listening for: A credible ramp plan that front-loads learning and relationship-building before big commitments; customer-outcome orientation from day one.
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 questions · 5
  1. It's one week before go-live and the customer's security team blocks your deployment over a review finding. Walk me through exactly what you do — technically and with the people involved — to get to go-live without cutting a corner you'll regret. Listening for: Treats security as part of the job, engages the review substantively, negotiates scope/timeline, and refuses to ship an unsafe shortcut under deadline pressure.
  2. Explain your end-to-end data-handling design for a customer with EU data-residency obligations under GDPR — where personal data may physically live, how you keep it in-region, lawful basis for processing, and how you'd prove all of it in an audit. Listening for: Command of data residency, cross-border transfer limits, lawful basis, and auditability — compliance breadth beyond HIPAA, defensible to a reviewer.
  3. I'm the customer's procurement officer and I think your statement-of-work pricing is too high. I'm pushing you to cut it. Defend your scope and your price to me. Listening for: Holds scope and price by tying them to value and effort; negotiates on scope rather than folding on either the moment procurement pushes.
  4. You're deploying an agent that will take actions inside the customer's own systems. How do you scope its access and build the audit trail so both you and their security team can sleep at night? Listening for: Least-privilege access design, action-level authorization/approval, and complete auditability for an autonomous actor — the agent-specific security surface, not generic app security.
  5. Walk me through the SOC 2 considerations for an embedded deployment at a regulated commercial customer — and then tell me what additionally changes if the customer is a US federal agency subject to FedRAMP. Listening for: Knows SOC 2 applies commercially and FedRAMP only to federal customers; can say how each reshapes architecture, access, and handover — not just name the acronyms.

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.

Salesforce (AI Deployment Strategist / FDE) Documented
Recruiter Behavioural (conflict · problem solving · growth mindset · scalability) AI technical (LLMs · RAG · trust layer · grounding) Communication evaluation

The AI technical round is scenario-based — design an agent for a retail or manufacturing customer, call out your assumptions, show scalability; grounding, the trust layer, and data accuracy are probed hard. The communication round simulates ambiguous questions from CTOs and IT managers. Salesforce confirms it runs no DSA round at all — senior candidates are assessed on solutioning and customer motivation, with coding weighted more only for junior candidates.

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: GoogleMetaGleanScale 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.

Why Interview Kickstart
25,000+
alumni network across tech
FAANG+
instructors — ex-Google, AWS, Databricks, Microsoft, Meta
1:1
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End-to-end
placement support
Practice the FDE loop with real mock interviews.

An Interview Kickstart advisor walks you through where you stand today, the exact gap to close, and the fastest route to a Forward Deployed Engineer offer — built around your background.

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