What Palantir and OpenAI say they look for, what each round of an FDE loop is really checking, and a drill for every round that you can run on your own.
I first published this on Medium. Cross-posting here because a lot of the people asking me about FDE interviews are on DEV.
By Pankit Kumar, Senior Data Scientist and Lead Instructor at NeuraPath Academy
A forward deployed engineer interview usually mixes five or six kinds of round: a phone screen with a coding question, an open-ended "decomposition" problem, a take-home you later defend, some work inside code you did not write, a customer scenario, and questions about why you want the job. Every one of them can be practised alone. You need a voice recorder, a timer, a few real job posts and the discipline to score yourself honestly.
That last part is where most preparation falls apart. People read question lists and walk in having never said an answer out loud.
What does an FDE interview actually look like?
There is no single FDE loop. So I went to the companies' own words first, then to one candidate account, and read all of it on 17 October 2026.
Palantir, where the role started, is the most open about its process. Its careers site has a set of "Getting Hired" pages. The one on the phone interview says the process "begins with one or two phone interviews, typically lasting 20 to 45 minutes each", and that for engineering roles "you'll also be asked a technical question that focuses on your coding abilities." It adds a line I wish every candidate read twice: "it's good to come up with a simple solution first and then go from there."
Palantir also publishes a page on navigating open-ended questions, which is the round prep sites call "decomposition", and another on working inside existing systems. Both are excellent free advice for this role.
OpenAI does not publish an FDE interview guide that I could read today, but its FDE job post tells you what the interview will be hunting for. The role will "own discovery, technical scoping, system design, build, and production rollout", and the post asks for people who "make trade-offs between scope, speed, and quality" and who "model calm and judgment when the stakes are high." Read those as interview rounds and they map almost one to one.
For the take-home, the clearest account I found is an anonymous candidate write-up on the prep site Aced (formerly Exponent). The candidate describes "a one-week take home, a live team discussion of that case study, and a separate AI-enabled coding screen that was basically an easy LeetCode problem." One account is one account, but it matches the job post, and the candidate's summary of what mattered is useful: the evaluation "seemed to come down to whether I could explain my decisions clearly and tie them back to customer needs."
Six common kinds of round, what each one checks, and a drill you can do without a partner. Loops differ; ask your recruiter which are in yours.
I could not find an official page from any of these companies describing a role-play round with an angry client. Prep sites describe one and the job posts clearly test for it, so I include it, but no published rubric sits behind it.
What is the coding screen really checking?
Mostly two things: that you can get to working code, and that someone listening can follow how you got there.
Palantir's phone interview page says it plainly: "Since we can't see you, the only way we can understand your thought process is to hear you talk." The OpenAI candidate called their coding round easy, then added that "they still wanted me to speak to time complexity and memory."
So the drill is about speaking. Pick two easy array, string or hashmap problems a day. Start a voice recording, say what the problem is, say the plain solution, write it, then say its time and memory cost. Play one recording back each day. You will hear ninety-second silences, and that silence is what an interviewer on a call experiences.
Keep the problems easy. Failing the coding round ends things early, but nothing I read suggests FDE loops reward exotic algorithms.
How do you practise the decomposition round with nobody across the table?
This is the round that makes FDE interviews different, so it gets the most space here.
Palantir describes it as handling problems "with multiple possible solutions that will incur different trade-offs", and gives its own advice for the 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."
Two failure modes sit inside that sentence: listing alternatives forever, and committing in the first minute to a design for a problem nobody has explained yet.
You do not need anyone to give you prompts. Palantir's own job posts contain them. Its new-grad FDE posting mentions problems such as "How can we analyze and adapt a global supply chain to deliver critical parts on time?" That is a perfect practice prompt: one line, no data, no scope.
Here is roughly how I would want a strong answer to that prompt to be shaped. Not the content, which depends on the client, but the order.
First, restate it in business terms. "Some parts arrive late, and when a critical one is late, a line stops or a customer waits. You want to see that risk early enough to act."
Then ask the questions that change the design. What makes a part critical, and who decides? Late against which date: the supplier's promised date, or the date the factory actually needs it? How do people find out about a late part today, and how many days before it bites? What data exists: purchase orders, supplier confirmations, shipment tracking, and how fresh is each? And the most important one: when a part is flagged, what can anyone actually do? Expedite it, switch supplier, move stock from another plant? The available actions decide what you build.
Then define success out loud, in terms the client can check. Something like: how many of the late critical parts did we flag, how many days ahead, and how often did a flag lead to an action. You agree the numbers with the client. You do not invent them in the interview.
Then cut a first version small enough to ship. One plant, the parts the planners already call critical, a daily refresh, and a ranked list that shows each part's risk and the reason for it. No machine learning until a simple rule (expected arrival later than needed-by date) has been measured.
Then sketch the whole path before going deep anywhere. Pull orders, confirmations and shipment events; join them on part and order; compute expected arrival against need date; rank; show it to one planner; log what they did.
Then name the trade-offs and the risks. Rules first versus a model first. Daily batch versus near real time. And the risk I would bet on: the same part carrying different numbers in different plants' systems, which quietly breaks every join.
Nothing in that answer requires knowing Palantir's software. It requires asking before building and committing before the clock runs out.
The drill I would use: reverse a finished spec
Here is the first-hand part, and it comes from our own classroom rather than an interview.
At the end of a live Python class on 12 September, our FDE fresher cohort got a brief called "Car Manufacturing & Delivery System". It was a coding exercise in classes, loops and dictionaries, and it was fully specified. Delivery cost had a base charge of ₹2,000, then ₹10 a kilometre for the first 500 km, ₹8 a kilometre from 500 to 1,000, and ₹6 beyond that. A hybrid car priced over ₹15 lakh was HIGH priority. There was a menu with eight options, a report format, and an extra challenge: let a customer place an order, and check whether enough cars are available before allocating them.
Read that brief as a forward deployed engineer and something jumps out. Every one of those rules is an answer to a question somebody had to ask. No dealership manager walks in with a distance slab table. Somebody sat with them and asked how delivery is charged today, whether long routes get cheaper per kilometre, which cars jump the queue and why.
So here is the drill. Take any fully specified exercise you have done, from a course, a college project or a hackathon. Cover everything except the one-line scenario. For our brief, that line was: "The company manufactures different cars and delivers them to dealerships across different cities." Give yourself fifteen minutes to write the questions you would ask the client. Then uncover the spec and tick off every rule your questions would have surfaced.
The rules you missed are your blind spots. I would expect pricing and priority rules to be among them, because those live in someone's head rather than in a system. The extra challenge hides the best question of all: what happens when two dealers order the last car of a model on the same day? That is a business decision, not a coding one, and an FDE has to notice it before writing the if.
How to score yourself
Record every decomposition attempt and play it back against six boxes. Did I restate the problem? Did I ask at least three questions before proposing anything? Did I say what success means? Did I cut a first version? Did I sketch end to end before going deep? Did I name a trade-off and a risk? Write the time of your first question as well. If the first two minutes are all solution, you know what to fix.
What happens in the take-home, and how should you rehearse it?
The OpenAI candidate's line is the one to remember: "the take home was basically the job." They built "a semantic search setup over Amazon products for ChatGPT", then walked the team through it.
The walkthrough is half the round, and it is the half people skip in practice. Pick a weekend project close to the work: search over a public product catalogue, a small assistant over a set of public documents, a pipeline that cleans an awkward public dataset. Build it in a fixed time, write a README a stranger could follow, and then present it to your phone camera in ten minutes. End with three things you would change if a customer asked for more precision, more speed or lower cost.
One caution about AI tools. Anthropic publishes guidance for candidates (last updated 10 July 2025). For take-home assessments it says: "Complete these without Claude unless we indicate otherwise." For live interviews, it allows "no AI assistance unless we indicate otherwise." The same page encourages using Claude to "practice your answers" while preparing. Other companies differ, and some allow AI in a coding screen, as the OpenAI candidate described. Ask every time, and practise both ways.
Why do FDE loops care about code you didn't write?
Because that is most of the job. Palantir's page on working inside existing systems says that "there are always third-party components in the mix", and that a developer "will spend far more time debugging, modifying, and extending existing code than writing brand-new things from the ground up."
The same page gives practice advice I have not seen bettered. Swap projects with a friend and see whether you can understand theirs without reading every line. Pick an open-source project, find a small bug in its issue tracker, and notice how long it takes to find the right place to fix it. And after you debug anything, ask yourself whether "there was a better way you could have found the bug."
That last question is the drill. Each time you fix something, log the symptom, your first wrong guess, what finally located the cause, and how long it took. After a month you know how you personally get stuck, and you have stories for two rounds.
How do you rehearse a difficult customer on your own?
Write ten short client prompts on cards. A few examples: "Your dashboard showed wrong numbers in front of my director." "We need this live by Friday, not in three weeks." "Our security team will not allow data to leave our network." "Can you also add a chatbot while you're at it?"
Draw one, start a voice memo, and answer as if the client is on the call. Then stop and write the follow-up email within ten minutes. Play the memo back and check three things. Did you ask what actually happened before explaining? Did you promise anything you could not personally deliver? Did you end with a specific next step and a time?
The OpenAI post asks for people who "spot risks early and adjust without slowing down" and who stay calm when the stakes are high. Calm is easy to claim and hard to fake on a recording. The memo will tell you the truth.
If you want a live opponent, an AI assistant can play the client during practice, and Anthropic's guidance explicitly allows practising answers that way. Just tell it to be difficult and to interrupt, or it will be far too polite.
What about the "why this role" questions?
Write five one-page stories about work someone else actually used; a college project counts. Note what you did, what went wrong and what you changed. Then replace every "we" that was really "I". A team story with no individual in it tells an interviewer nothing about you.
A four-week plan you can run alone
About seven to nine hours a week, spread across six drills. A suggestion to adapt to your own loop.
Decomposition gets steady time every week because it improves slowly and people under-practise it. Coding tapers after week one. The take-home sits in the middle so you have something to present in week four.
Who this is not for
If your interview is in three days, skip the plan. Do two decomposition prompts out loud tonight, one customer memo tomorrow, and rest the day before.
If you have never written a program that someone else used, the interview is not your bottleneck yet. Build one small thing for a real user first, and come back to this when you have a story to tell about it.
And if you are aiming for an AI lab role that asks for five or more years of engineering or deployment experience including customer-facing work, as the OpenAI post does, no amount of interview practice replaces that experience. Practise anyway, but apply to roles that match where you are.
What to do this week
- Ask your recruiter, or a friend at the company, which rounds are in the loop and how long each runs. Most will tell you.
- Do the reverse-the-spec drill once on a project you have already finished.
- Answer the supply chain prompt above out loud in 45 minutes, record it, and score it on the six boxes.
- Record two customer memos and write both follow-up emails.
- Pick three real FDE postings and underline the phrases that describe a round. "Own discovery" is a decomposition round. "Calm when the stakes are high" is a customer round.
For that last step, we keep a free list of FDE and related openings in India at neurapath.in/forward-deployed-engineer/jobs, pulled from employers' own careers pages and refreshed every few hours. On 17 October 2026 it listed 137 open roles at 83 companies, a fraction of all postings. You can filter by city, experience and skill, and if you leave your email you get today's roles straight away and then one email a week with only the new ones.
A question for the comments: which round in your FDE or solutions loop surprised you most, and what would you practise differently? I am especially curious whether anyone met a live customer role-play, because I could not find a company that documents one.
Pankit Kumar is a Senior Data Scientist at Parexel with ten years in data science and AI, much of it building production systems in regulated pharma and clinical settings. He leads the forward deployed engineering programmes at NeuraPath Academy in Noida.
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