The FDE decomposition interview: the method on one page

The interviewer says: “A hospital network’s patients who need an interpreter wait too long, and some appointments go ahead without one. The director of patient experience wants an AI translation app on every ward tablet. Go.” The strong first sentence isn’t about the app. It’s a question: “When you say too long, from when to when?” Here is the rest of that answer, in the words to say.

That brief is a decomposition round: a customer problem with more than one reasonable answer, and a clock. The method and rubric here are ours; no employer publishes one.

It fits both kinds of FDE role: the sponsor wants an AI app, as an AI lab’s customer would; the lever is scheduling data, enterprise work. The Pragmatic Engineer reported, in August 2025, that Colin Jarvis, then OpenAI’s head of forward deployed engineering, said what customers describe in scoping often doesn’t match the data and system reality. ^[1]Source 1What are Forward Deployed Engineers, and why are they so in demand? (Gergely Orosz)PublisherThe Pragmatic EngineerSource typenews report That is this round: the director describes an app; the data says booking.

What Palantir publishes about open-ended questions

Say Palantir’s advice back as a plan: two options, a choice, a first version.

Palantir’s careers page on open-ended questions calls them “a key part of the process,” and its only interview advice is this: “When discussing an open-ended problem in an interview, articulate the alternatives and trade-offs, but don’t forget to be pragmatic enough to be able to arrive at some concrete approach. Deliver a functioning idea first, then expand it afterwards.” ^[2]Source 2Palantir Careers | Navigating Open-Ended QuestionsPublisherPalantir TechnologiesSource typecompany hiring page

By the end of the interpreter case, that advice sounds like this:

Candidate: “Two ways to cut the wait: a translation app, or booking interpreters the evening before. I’ll start with booking, because it fixes the appointments that go ahead with nobody. The first version is tomorrow’s list; once the scheduler uses it, we grow it.”

What candidates report being asked

Expect a customer problem, with or without data, and adjust your first move.

Palantir’s careers page for students lists its virtual onsite as two one-hour interviews, learning and decomposition. ^[3]Source 3Students | Palantir Careers (timeline image: RecruitingTimeline_V3.jpg)PublisherPalantirSource typecompany hiring page One candidate reported on Aced, in August 2026, that the decomposition round was the most important in their entry-level Databricks FDE loop, where they “had to treat the interviewer almost like a client and clarify stakeholder, scope, and KPI before touching architecture.” ^[4]Source 4Databricks Forward Deployed Engineer Interview ExperiencePublisherAced (formerly Exponent)Source typecandidate’s personal write-up

At the vague end, First Round Review reported, in February 2026, that Palantir’s former FDE recruiting lead said many of its FDE questions were high-level problem solving on customer problems, such as explaining insider trading and asking the candidate to design a solution: what data they would need, and what they would ask the customer. ^[5]Source 5So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report Our insider trading post works it through.

At the data end, two Palantir new-grad candidates on Blind said the round came with data. ^{[6, 7]}Source 6Palantir Learning & Decomposition Interview (Blind)PublisherBlindSource typecandidate report on BlindSource 7Update: interview experience - Palantir new grad FDSE interview (Blind)PublisherBlindSource typecandidate report on Blind One candidate wrote on Blind, in an update to an October 2022 post, that with London taxi data you had to “suggest idea, draw system components, come up with required apis and code if required, for a solution that can be developed and deployed in a week.” ^[6]Source 6Palantir Learning & Decomposition Interview (Blind)PublisherBlindSource typecandidate report on Blind That is the method: a first version, its parts, its interface, its code.

With no data, ask: “What does the customer already collect, and who owns it?” With data, say what the columns are missing before proposing anything.

Case interview or system design? Reports differ

Open like a case interview and finish like a design review.

A Blind commenter, in April 2022, called Palantir’s Deployment Strategist decomp “very similar to consulting case interview.” ^{[8, 9]}Source 8Palantir Deployment Strategist Interview (Blind)PublisherBlindSource typecandidate report on BlindSource 9Palantir Deployment Strategist (Blind)PublisherBlindSource typecandidate report on Blind Yet an Aced report of a Palantir Deployment Strategist loop in September 2025, which ended in an offer, says expecting a consulting case was the wrong mindset, and three other Blind posters, one going only on what they had read, compared decomp with system design. ^{[7, 10, 11, 12]}Source 7Update: interview experience - Palantir new grad FDSE interview (Blind)PublisherBlindSource typecandidate report on BlindSource 10Palantir Deployment Strategist Interview ExperiencePublisherAced (formerly Exponent)Source typecandidate’s personal write-upSource 11Palantir Decomposition Question (Blind)PublisherBlind (Teamblind)Source typecandidate report on BlindSource 12Palantir FDSE Interview (Blind)PublisherBlind (Teamblind)Source typecandidate report on Blind First Round Review reported, in February 2026, that Palantir’s former FDE recruiting lead said the interviews aimed to assess “both their business reasoning and technical reasoning.” ^[5]Source 5So You Want to Hire a Forward Deployed EngineerPublisherFirst Round ReviewSource typenews report

The method, step by step

Say each step’s opening line as you enter it, a handoff as you leave, and a four-part close. A Blind user who said they had worked at Palantir suggested a similar arc, in March 2025, after saying it’s difficult to give a one-size-fits-all answer: ask questions, break the problem down, build a practical solution, then expand that v0, the first working version. ^{[12, 13]}Source 12Palantir FDSE Interview (Blind)PublisherBlind (Teamblind)Source typecandidate report on BlindSource 13Palantir Interview London HELP !!! (Blind)PublisherBlind (Teamblind)Source typecandidate report on Blind

1. Clarify. “Before I design anything: when you say too long, from when to when?” The director means complaints, interpreter services means request to arrival, the patient means check-in to an interpreter in the room. Here, waits cluster in rarer languages and afternoon clinics, and consent needs a qualified interpreter.
2. Stakeholders and metrics. “Who decides, who uses it, who pays and who could block it?” The director decides; the scheduler and nurse managers use it; interpreter services pays; compliance and IT security could block it. Metric: appointments started with no interpreter, per week; guardrail: interpreter cost. Nobody knows today’s value, so measure it first.
3. Inputs. “For each source: who owns it, how fresh is it, what’s wrong with it, and who approves access?” The request log is typed after the fact, with no appointment ID, so match on medical record number (MRN) in the two days before each request: one match is clean, two ambiguous, none unmatched.
4. Workstreams by risk. “The riskiest unknown goes first, and here’s the order I rejected.” Here it’s whether waits are concentrated: if so, booking ahead fixes them; if not, it’s staffing. Measure where the wait is; ask for schedule access on day one. The app-first order loses: the app can’t show where the gaps are.
5. Walking skeleton. “Here’s the thinnest version a real user could use soon.” Each evening, interpreter services and the nurse managers get tomorrow’s appointments needing an interpreter, with the one assigned or “none yet.” Within two weeks it shows whether booking ahead closes most gaps. Next, AI goes where a mistake gets caught: it drafts reminder templates in the patient’s language, a qualified interpreter approves or corrects each one once, and the corrections become the test set. Never in consent conversations.
6. Failure modes. “How could this fail, and how would we know early?” Data: a blank or wrong language field, so unknowns stay on the list, and their count at check-in sizes it in week one. Harm: an interpreter booked in the wrong language, caught by mismatches interpreters report. Organizational: interpreter services, judged on cost per minute, may not book paid video interpreters; the signal is gaps still open when clinic starts.

A handoff carries one decision forward:

Candidate: “That’s enough to commit. The first version is an evening list, and I’m assuming the language field is mostly filled. Next: who’s involved.”

Pushed on the app, keep it a hypothesis:

Interviewer: “The director has budget for the app. Why not just build it?”

Candidate: “It stays on the table. First: of last month’s appointments with no interpreter, how many were in languages the app handles? If most, the app goes first.”

Interviewer: “Nobody has that number.”

Candidate: “Then finding it is the first workstream, and the evening list uses the same data. The app waits for the answer.”

When time is nearly out, close in four parts: the problem, the first version, the biggest risk, and Monday.

Candidate: “To close: the wait is late booking for a few languages in afternoon clinics, not translation. The first version is tomorrow’s list each evening; the next adds AI reminders an interpreter approves. The biggest risk is a blank or wrong language field, so unknowns go on the list. On Monday I’d ask for read access to the schedule.”

Concreteness runs through every step: entities with their keys, one interface, and one volume estimate that decides a choice. Say the entities aloud:

patient(mrn, preferred_language)
  language set once, at registration
appointment(appointment_id, mrn,
            clinic, starts_at)
request_log(request_id, mrn,
            language, typed_at)
  no appointment_id
Volume: ~1,500 appointments a day,
a few hundred on the list

If asked, write the core query: tomorrow’s appointments joined to patients on MRN, anyone not English, unknowns kept.

SELECT a.starts_at, a.clinic, a.mrn,
       COALESCE(NULLIF(TRIM(p.preferred_language), ''), 'unknown')
         AS language
FROM appointment AS a
JOIN patient AS p ON p.mrn = a.mrn
WHERE a.starts_at >= :tomorrow AND a.starts_at < :day_after
  AND COALESCE(NULLIF(TRIM(p.preferred_language), ''), 'unknown')
      <> 'English'
ORDER BY language, a.starts_at;

The scheduler types in the interpreter column: that’s what the first version fakes. A few hundred rows a night means one query on the reporting copy, no pipeline.

A second query counts each request’s candidate appointments. The share with none, or two or more, is your first data-quality number.

WITH matched AS (
  SELECT r.request_id, COUNT(a.appointment_id) AS candidates
  FROM request_log AS r
  LEFT JOIN appointment AS a
    ON a.mrn = r.mrn
   AND a.starts_at <= r.typed_at
   AND a.starts_at >= r.typed_at - INTERVAL '2 days'
  GROUP BY r.request_id
)
SELECT LEAST(candidates, 2) AS candidates, COUNT(*) AS requests
FROM matched
GROUP BY 1
ORDER BY 1;

The board you fill as you go

Write each fact on the board the moment it arrives, so it shows how you got there.

You say six steps; the run screen numbers five, with a Risks lane under Workstreams, because you name risks as you order the work. Our scorer reads the board too.

The interpreter board at the end of a run, in the run screen’s order; [A] marks a labeled assumption.

Clarify
  "Too long" = check-in to
  interpreter in the room
  [A] language field mostly filled
Stakeholders
  Director decides; scheduler,
  nurse managers use; interpreter
  services pays; compliance blocks
  Metric: appointments started
  with no interpreter, per week
  Guardrail: interpreter cost
Inputs
  Request log: no appointment ID;
  match on MRN, count misses
Workstreams
  1 Where is the wait?
  Schedule access: ask day one
Risks
  Language blank or wrong:
  unknowns on the list
  Wrong-language interpreter
  Video not booked: open gaps
Skeleton
  Evening list: assigned or none yet

What to fix first

Take your lowest gate below level 3, or else your lowest score, and rehearse its fix before your next run.

In our rubric, clarify before solving, walking skeleton and technical concreteness are gates. Pass: a weighted score at level 3 or more, every gate at level 3 or more, nothing below level 2. Lean yes: a weighted score at level 2.3 or more, every gate at level 2 or more, nothing at level 0. Anything else is Not yet. The 10-minute run scores every dimension but gives no verdict. Without Pro, the scorecard’s next step links back here. A verdict is our rubric’s training signal, not a prediction of any company’s decision.

Each dimension has one habit that fixes it and one lesson that teaches it:

For each gate, a weak line and a strong one:

• Clarify. Weak: “What’s the budget and timeline?” No answer changes what you build. Strong: “When you say too long, is that request to arrival, or appointments that start with nobody? The second changes what I build.”
• Walking skeleton. Weak: “We’d build a scheduling platform.” Nobody uses it soon. Strong: “Tomorrow’s list, emailed each evening to interpreter services, one row per appointment, the assigned interpreter typed in by hand.”
• Technical concreteness. Weak: “We’d pull the data into a warehouse.” No table, no size. Strong: “Appointment joins patient on MRN. A few hundred rows a night, so one query, no pipeline.”

What the method is not

Use the steps as signposts, not a script.

A March 2025 Blind post by a self-described former Palantir employee gives a recipe for a bad performance: no questions, large assumptions not checked with the interviewer, misunderstanding the problem, impractical solutions, and not expanding or optimizing the v0. ^{[7, 12]}Source 7Update: interview experience - Palantir new grad FDSE interview (Blind)PublisherBlindSource typecandidate report on BlindSource 12Palantir FDSE Interview (Blind)PublisherBlind (Teamblind)Source typecandidate report on Blind Three shapes here:

• The app builder designs speech-to-text for the ward tablets and never finds the appointments that started with nobody.
• The consultant spends the round on stakeholders and never names a table.
• The architect proposes streaming and a lakehouse for a few hundred rows a night.

If the interviewer steers you elsewhere, follow. When your questions confirm the ask, say so and build it.

Ask your recruiter: “Will the open-ended round come with a dataset, and should I expect to write code or an API?”

Now run Building permits take months as the 10-minute case, with the step names down the side of your notes. The mayor has already promised an AI review tool: treat it like the translation app, a hypothesis to test against where the time goes.

Keep going. The permits case is free once you sign in, including one full-length run with a verdict. Pro shows the worked run of the same case: the question that found the real lever, and where each dimension earned its score.