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 what each round tests, how to attack it, what gets people rejected, and the real problems you'll be handed.

5–7 rounds, 3–6 weeks 65 real problems 11 stages documented 7 company loops

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Round by round

A full loop runs 5–7 rounds over three to six weeks, and no two companies slice it the same way. The 11 stages below are the menu the field draws from; any given employer runs some of them. Open one for what it tests, how to attack it, what gets you rejected, and the problems you will actually be handed.

Tagged by what the round costs you: Deepest prep weeks of it Decides the offer the round most offers turn on Rehearse it stories and role-play, out loud Refresh and drill known ground, brought back up

A blue left edge marks a round with no equivalent in a normal software interview.

01 Recruiter & HM Screens Why forward-deployed work and not a regular engineering role, and whether you actually owned the projects on your résumé. Rehearse it 5 problems

What it 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.
How to attack it	Have a crisp, personal answer for why customer-facing technical work pulls you. 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.

Interview problems · 5

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?
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.
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?
The bar on résumés has moved: a web app or a CRUD service does not clear this screen any more. Which project on yours would you put in front of a customer, and what did it actually do once it was running?
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.

02 Take-Home Build Build something real on your own clock, then defend it live. The walkthrough is a rehearsal for presenting to a customer. Deepest prep 6 problems

What it tests	A build assignment you do unsupervised, anywhere from a few hours to a few days. Rarely just code, either: expect a repo that runs, and increasingly a recorded video walking through what you built. Scoring happens asynchronously and weighs production-readiness as heavily as correctness, after which a live deep-dive puts you in front of the people who read it and asks you to account for the choices you made. The recording is the point. An FDE presents work to customers constantly, so this round tests that directly.
How to attack it	Treat it as a customer deliverable, not homework. Ask your clarifying questions before you start, then double whatever time estimate you first landed on, because you will be wrong about it. Decide how you will show the thing works before you build it: starting without an evaluation is the fastest way to fail this round. Ship what survives messy input. Something clever that half-runs loses to something plain that holds, and in the walkthrough you lead with what the customer gets, then the design decisions and what you threw away.
Rejected for	A prototype with no evaluation, no error handling and no README. A walkthrough that narrates the code line by line instead of explaining the decisions. Assumptions you made silently instead of asking about.

Interview problems · 6

Here is our public repo and three open issues. Fix them, then record a short video walking us through what you changed and why you took that approach. Both the code and the recording get reviewed after you submit.
You have about five hours and our public API. Build something a customer in this industry would actually find useful, then present it to us as though we were that customer.
Build a retrieval assistant over the document set attached. Every answer has to cite the source it came from, and it has to say it does not know when the corpus cannot answer the question. Send us a repo and a short walkthrough.
Build an agent that can query a database, search documents and run shell commands. Anything destructive needs a human approval step. Show us how you tested that the approval gate holds.
Take this folder of scanned forms and produce structured JSON with a confidence score per field. Tell us which fields you would not let through without a human checking them.
Build a router in front of three model providers with caching and failover. It should hold at a hundred requests a second with p95 under two seconds. Show us your numbers.

03 Practical Coding Practical engineering at a comfortable medium, set inside a real customer mess instead of an abstract puzzle. Refresh and drill 8 problems

What it 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. Expect Python. The choice of language has largely gone, because the customer stack is Python, and the editor is often a plain shared document with no autocomplete, no highlighting and no error formatting. The code is expected to run, so import syntax has to come from memory and pseudocode does not pass.
How to attack it	Clarify the spec before typing. Narrate every decision. Favour integration-grade code that survives contact with a real system (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. Reach for classes when the problem has state and several kinds of thing interacting, rather than forcing everything into one long function.
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.

Interview problems · 8

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.
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.
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.
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?
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.
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?
Model this in Python for me: a customer's order moves through a set of states, each state allows a different set of operations, and some transitions are not legal. Design the classes and show me how the objects talk to each other.
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.

04 AI System Design The one to prepare hardest Design an AI system for one customer with real constraints: private deployment, identity, compliance, and how you'd prove it works. Deepest prep 7 problems

What it 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.
How to 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.

This is the round to prepare hardest, and it is not really one round. The take-home, system-design debugging and AI security rounds are the same muscle pointed at different moments: designing an AI system for one real customer, then defending it when it breaks or gets attacked. Together those 4 rounds are 25 of the 65 problems on this page. The decomposition round still decides the offer; this is the one that takes longest to be ready for.

Interview problems · 7

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.
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.
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.
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?
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 what each trade-off costs you.
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.
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?

05 Decomposition / Case Study Turn a vague, enormous problem into a plan someone could start on Monday. No code. This round decides most offers. Decides the offer 6 problems

What it 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.
How to 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.

Interview problems · 6

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.
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?
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?
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?
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?
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.

06 System-Design Debugging Something is failing and you get almost nothing to go on. They score whether you reason about what's likely or just run a checklist. Deepest prep 6 problems

What it 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.
How to 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.

Interview problems · 6

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.
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?
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.
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?
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.
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?

07 Client Simulation The interviewer plays the customer and the conversation is a hard one. Hold it live, out loud. Rehearse it 5 problems

What it 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.
How to 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.

Interview problems · 5

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.
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.
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.
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.
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.

08 AI-Assisted Pair Coding Build live with an AI assistant while they score what you accept and what you throw away. Rehearse it 5 problems

What it 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.
How to 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.

Interview problems · 5

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.
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.
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.
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.
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?

09 Behavioral & Values (STAR+) Ownership with a customer in the story, and whether you fit how the company actually works. Rehearse it 6 problems

What it 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.
How to 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.

Interview problems · 6

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?
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?
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?
Describe a time you drove alignment across a team you didn't manage, no authority, competing agendas. How'd you actually move them?
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?
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.

10 AI Security & Safety Whether you can stop an agent being turned against the customer who deployed it. Deepest prep 6 problems

What it tests	Security at the model layer, which is a different body of knowledge from the compliance review in the procurement round. The questions are about how an LLM system gets attacked through its own inputs, and what you build so a compromise stays contained. No round in the loop has grown faster. The reason is plain enough: FDEs now hand customers agents that hold real credentials and take real actions inside their systems.
How to attack it	Separate the way in from the damage done. Injection is how an attacker gets in; how much authority the agent holds is what makes it expensive. Treat everything the model reads as untrusted, the retrieval corpus and the descriptions of its own tools included. Put the limits in code. A prompt is advisory, and an attacker gets to write prompts too. Finish by saying what you would monitor, and what would make you block a launch.
Rejected for	Treating prompt injection as something better instructions will solve. Naming frameworks without running a single concrete attack through one. Guardrails that live only in the system prompt. Claiming a system is safe without saying how you would know it had been attacked.

Interview problems · 6

Explain the difference between direct and indirect prompt injection, and then tell me which of the two you would lose sleep over in an enterprise deployment.
Your agent reads from a customer document store that anyone in the company can add to. Someone plants instructions inside a PDF. How do you stop that turning into an action the agent takes?
PII enters this pipeline in four places: what the user types, what retrieval pulls back, what the tools return, and what the model emits. Design redaction for all four, and tell me how the user still gets a readable answer.
You are deploying an agent that can issue refunds and update customer records. Design the layers that stop it doing something expensive, and tell me which of those layers live in code rather than in the prompt.
Walk me through how you would red-team this customer's chatbot before go-live. What do you try, in what order, and what finding would make you block the launch?
The customer asks whether you train on their data. Give me the exact answer you would give them, and explain what zero data retention does and does not change.

11 Procurement / Security Their security reviewers, their auditors and their procurement officer, all standing between you and go-live. Refresh and drill 5 problems

What it 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.
How to 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.

Interview problems · 5

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.
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.
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.
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?
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.

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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.

Level	Coding load	What 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.

How real companies run it

The shape is shared, but every company tilts it. Below are the documented loops, flagged by how much we can evidence, so you prepare on facts rather than folklore.

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, so 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 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: GoogleMetaAmazonMicrosoftNVIDIAGleanScale AIRampSierraPostmanStripe

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