An FDE interview should show whether a candidate can take an unclear operating problem, build the right technical solution, and make it work with the people who will use it. A coding exercise alone cannot answer that question. Start with the work this hire will own, then use the same evidence and rating scale for every candidate.
Write the scorecard before scheduling interviews
Describe the outcomes for the first six to twelve months. Examples include putting an AI workflow into production, integrating a customer’s systems, improving a deployment process, or helping an internal team adopt a tool. Avoid outcomes that depend on a future product decision the candidate cannot control.
Turn those outcomes into four or five observable areas:
- Technical delivery: Can the candidate reason about APIs, data, failure modes, security, testing, and operations at the level the role requires?
- Problem framing: Can they separate the customer’s stated request from the operating problem and define a useful first version?
- Working with users and partners: Can they ask precise questions, explain trade-offs, and change the plan when evidence changes?
- Ownership: Can they carry work from discovery through rollout, monitoring, documentation, and handoff?
- Judgment: Can they identify what must be solved now, what can wait, and when to involve product, sales, security, or another engineer?
Set a clear scale before interviews. For example, “insufficient evidence,” “meets the role,” and “strong evidence for this role” are easier to apply than a score whose meaning changes from interviewer to interviewer. A candidate can be technically capable and still lack evidence for the customer-facing or production responsibility in a particular FDE role.
Ask for evidence from past work
Use the same core questions for every candidate. Ask follow-ups that clarify the candidate’s own contribution, the constraints, and what happened after launch.
- Tell me about a project where the request was incomplete or changed during delivery. What did you learn first, what did you build, and what changed after users tried it?
- Tell me about a deployment or integration that failed or needed a rollback. How did you find the cause, communicate it, and prevent a repeat?
- Describe a time you had to explain a technical limitation to a customer or partner. What decision did the explanation support?
- Which part of the system did you own after it went live? How did you monitor it and decide whether it needed more work?
- Tell me about a decision you escalated. What information did you bring, and what would have happened if you had acted alone?
Ask for artifacts when the candidate can share them safely: an architecture sketch, runbook, incident summary, or anonymized implementation plan. Do not ask candidates to disclose a former employer’s confidential information. Record what the candidate did, the evidence they used, and the result. Do not convert confidence or familiarity with interview language into a proxy for delivery skill.
Use a work sample that matches the job
Give the candidate a short, fictional scenario based on the role’s real work. A production AI role may involve an unreliable model output and a request to ship quickly. A data and integration role may involve conflicting schemas, an unavailable API, and a customer deadline. State what information is available, what is missing, and how much time the exercise should take. Tell the candidate whether they may ask questions.
Evaluate the reasoning, not a hidden “correct” architecture. Look for whether the candidate:
- asks about users, constraints, data quality, access, and failure impact;
- proposes a small test or first release that can produce evidence;
- names privacy, security, reliability, and rollback concerns where they apply;
- explains how they would measure whether the solution works;
- makes ownership and handoff clear; and
- communicates the trade-offs in language the customer or operating team can use.
Keep the task bounded. Unpaid work that resembles a real customer deliverable is a poor substitute for an interview and makes the hiring process harder to compare. A live discussion, a short take-home with a stated time limit, or a review of an anonymized prior artifact can all work when the evaluation criteria are explicit.
Make the decision reviewable
Have interviewers submit notes independently before the debrief. Each note should link a conclusion to an observed answer, artifact, or exercise decision. Separate “we did not test this” from “the candidate did not meet the bar.” If the role requires travel, customer contact, on-call work, a security clearance, or a particular work location, explain and assess that requirement directly.
At the debrief, compare evidence against the scorecard. Do not average unrelated opinions into a number. If the panel disagrees, identify the missing evidence and decide whether another focused interview can resolve it. If the evidence is still mixed, record the risk and the support the team would need after hiring. A hiring decision should answer: can this person do the defined work, under the stated conditions, with a reasonable plan for the gaps?
Related reading: how to hire an FDE, how to write an FDE job description, and how to prepare for an FDE interview.