How ready are you for an FDE interview?
Fifteen scenarios from real Forward Deployed Engineer loops. You will find out which of the eleven areas these interviews test you have actually worked with, which gap is costing you most, and exactly what to do about it. Every answer tells you what that choice signals to an interviewer, so the fifteen minutes are worth it even if you stop halfway.
Which role are you actually going for?
The titles overlap, and the loops behind them do not weight the same things.
The eleven areas an FDE loop tests
Forward Deployed Engineer interviews do not test what a standard software loop tests. They weight the rounds below, and they weight them differently depending on whether you are aiming at a frontier lab, a Palantir-style deployment role, a data platform or an early startup. The assessment above works out which of these you have covered.
- Retrieval & agentsdecides loops
- The modal FDE design round. Almost every deployment is a retrieval system with something agentic bolted on, and almost every one fails quietly at retrieval rather than loudly at the model.
- Customer & communicationdecides loops
- The round strong engineers most often fail. Scoping a problem the customer cannot articulate is the actual job, and it is graded in every loop whether or not it has its own stage.
- System design & productiondecides loops
- Not the distributed-systems interview you have prepared for. The FDE version starts from someone else's infrastructure, someone else's data, and constraints you did not choose.
- Practical codingdecides loops
- Graded as a deliverable, not a puzzle. Loops here reward code someone else could run and extend, which is a different skill from solving the problem quickly.
- LLM fundamentalsexpected
- The vocabulary tax. You are rarely asked to derive anything, but being unable to reason about context, tokenisation or sampling costs you credibility in every other round.
- Evaluation & MLOpsdecides loops
- The single strongest hiring signal in an FDE loop is raising evaluation before you are asked. It is also the question most candidates answer worst.
- Security & governanceexpected
- What turns a working prototype into something a regulated customer is allowed to run. Deployment topology and data boundaries decide the architecture before any code exists.
- SQL & data engineeringexpected
- The customer's data is the actual terrain. Data-platform loops weight this heavily, and every deployment runs through someone's warehouse eventually.
- ML product designexpected
- Turning a vague business outcome into a system with a measurable objective. The round that separates people who ship features from people who ship results.
- ML & data sciencesituational
- Rarely the centre of an FDE loop, and regularly the thing that decides a follow-up question. Enough to be dangerous is genuinely the bar here.
- ML infrastructuresituational
- Matters enormously at frontier labs and self-hosted deployments, and barely at all elsewhere. Worth knowing which loop you are walking into.
Profiles this tends to find
The result names the pattern in your answers rather than ranking you. These are the shapes that come up most, and each one has a different fastest path.
- The Builder Who Has Not Met the Customer
You can build the thing. Your answers on architecture and code are the answers of someone who has shipped, and then the customer questions land differently: you reach for the technical move where the round is testing whether you can sit in an unclear conversation without resolving it too early.
- The Consultant Who Needs Reps in Code
You are unusually good at the half of this job most engineers find hardest. You scope, you handle bad news, you think about who is in the room.
- The Demo Builder
You are fluent in the modern stack and you move fast, which is exactly what gets a prototype in front of a customer. What is missing is the layer that turns a prototype into something a serious buyer is allowed to run: evaluation that a sceptic would accept, and the boundaries that decide the architecture before any code exists.
- The Platform Engineer Crossing Over
Your instincts about data and systems are the ones this job runs on, and they transfer better than you probably think. What the loops will test is whether you can reason about model behaviour rather than route around it, and that is a vocabulary gap rather than a capability gap..
- The One Who Asks How You Know
You reached for evaluation and boundaries without being prompted, which is the single strongest signal in an FDE loop and rarer than it should be. Your remaining risk is not knowledge.
- The Strong Generalist
There is no obvious hole here, which is a genuinely good position and a slightly awkward one to prepare from: with no glaring gap, most people default to revising what they already know. Your risk is arriving broadly competent and not distinctly strong in the rounds that actually decide the offer..
Where the plan sends you
Whatever your result, it is built from the same library. You can also just start reading.
You could ask a model instead. Here is the difference.
Genuinely, ask one. It will give you a good general answer about Forward Deployed Engineer interviews, and if you are early it may be all you need. Four things it will not do.
It will agree with you
Say you would add a reranker and a model will tell you that is a solid instinct. Here it is scored as premature, with the reason: reranking helps when the right passage comes back at rank eight and does nothing when it never comes back at all. Every option in this assessment was weighted by what it signals in a real loop, which means it is built to disagree with you when disagreeing is the useful thing.
It answers the question you asked
You cannot ask about the gap you do not know you have, and the gaps that fail people in these loops are almost always the ones they never thought to raise. This works the other way round: it probes all eleven areas whether or not you would have brought them up, which is the entire point of a diagnostic.
It can describe a plan, not give you one
A model can tell you to study retrieval evaluation. It cannot hand you the concept, the four questions that test it and the lesson that covers it, because it does not have them. Your plan resolves to real material that exists here and is maintained: 606 questions with worked answers, 157 concepts and 125 course lessons.
It does not remember
Come back in six weeks and a chat is gone. Your result is a stable artifact: the same diagnosis, rebuilt from your answers, with links that still point at content that exists. Sign in and it is waiting for you.
None of this is a claim that models are bad at explaining things. They are very good at it. It is a claim that explaining and diagnosing are different jobs, and that the second one needs a fixed instrument, calibrated answers and a library behind it.
Why this measures coverage and not ability
A multiple-choice question cannot tell you whether someone can run a discovery conversation, and pretending otherwise would be the fastest way to lose the engineers this is built for. So nothing here produces a score out of a hundred. It reports which areas your answers show you have worked with, which is something a fixed set of scenarios can honestly support, and it is the more useful half anyway: you can act on a gap, and there is nothing to do with a number.