Forward Deployed Engineer Interview Guide

The interviewer leans back and says, "We're a global software company. We want an AI agent to automate support. You have eight weeks." Then they wait.

If you start drawing architecture boxes, you've probably already lost points. They wanted to see whether you'd ask what "automate support" means first.

That moment is the whole forward deployed engineer (FDE) interview. It tests two jobs at once: building production software, and figuring out, scoping, and leading a customer project when the problem isn't fully formed yet.

Most candidates prepare for one half. Strong engineers jump to architecture before they've found the real problem. Strong customer-facing candidates talk well but can't show they've built and run real systems. The loop is designed to expose that gap.

What they're looking for

Job postings from OpenAI, Palantir, and Scale AI (linked at the bottom) point at the same things: delivering a project end to end, writing production code, working with customers, handling vague problems, making technical calls, and feeding what you learn back to the product or research teams. The format changes by company. What they're looking for doesn't.

What they check	Strong signal	Weak signal
Production engineering	Correct code with tests, logging, and failure handling that someone else can maintain	Demo code that breaks the first time something goes wrong
Breaking the problem down	Finds the decision that matters and tests the riskiest assumption first	Builds exactly the feature that was asked for
Customer discovery	Finds the users, how the work flows, how it performs today, who owns it, and what limits apply	Asks broad questions that don't change the plan
Product judgment	Keeps scope tight and separates one-off work from reusable work	Promises everything, or refuses any custom work
Systems thinking	Covers data, permissions, reliability, security, cost, and day-to-day running, each in proportion	Names technologies without saying what they trade off
Delivery leadership	Puts the work in order, says where the risks are, and defines when to stop	Assumes everyone agrees and access will be easy
Communication	Explains decisions at the listener's level	Uses jargon or hides uncertainty
Learning	Changes direction when the evidence changes	Defends the first plan after the facts move

The rounds you'll usually see

1. Recruiter or hiring manager screen

They're checking why you want the job, whether you understand it, the logistics, and whether you've really done customer-facing engineering.

Have a two-minute answer for why FDE fits you, including what you'd be giving up. Put one example in it where you turned a vague request into something that ran in production.

Expect questions like:

Why FDE instead of product engineering or solutions architecture?
Tell me about a system you owned end to end.
How much customer or user interaction have you had?
How do you feel about travel and switching industries?
What kind of ambiguity energizes you, and what kind creates risk?

2. Production coding

This might be algorithms, practical application code, debugging, or a take-home. Ask the recruiter which. Write clear code, say your assumptions, test the normal and edge cases, and talk through the trade-offs.

For practical tasks, show how you'd handle bad input, retries, permissions, logging, and failures, but only where they matter. One that comes up often is idempotency: making sure a request that arrives twice doesn't do the work twice. Don't bury a simple problem under a framework.

3. Systems design

You'll often be designing a customer's workflow, not a generic platform. Start with the result they want, the volume, the users, the data, who's allowed to see what, how fast it has to be, how often it can be down, and what happens when it fails. Then propose the smallest design that meets those.

Cover:

Where the data comes from and how good it is
Logins, permissions, privacy, and audit records
APIs, events, stored state, and what the system depends on
Reliability, fallbacks, monitoring, and support
Scale, speed, cost, and deployment
How you move people from the old way to the new one
For AI: how you'll test output quality (evals), which model handles what, how it looks up information (retrieval), which tools it's allowed to use, and where a person reviews

4. Customer discovery or case

The interviewer plays a customer with a broad request. You're not there to impress them with a solution. You're there to sharpen the problem and agree on a believable first step. The next two sections show how.

5. Behavioral and delivery judgment

Expect questions about conflict, failure, scope, pushy customers, incidents, learning something new, priorities, and getting people to act when you're not their boss.

6. Executive or cross-functional round

You might explain the same problem to a business leader, product, research, security, or sales. Change the depth. Keep the decision and the risks the same.

How to run the discovery conversation

Run it like a normal conversation, not a checklist you read out loud. Cover eight things.

Outcome. What should get better, for whom, and why now? How is it measured today?
Workflow. What starts the work? Who does each step? What decision or action ends it? Where are the delays and errors?
Users and owners. Who uses the system, who manages the process, who owns the number, and who can accept risk?
Data and systems. Where does the information live? What's missing or unreliable? Which systems need to read, write, or approve?
Constraints. Security, privacy, regulation, speed, cost, timeline, purchasing, geography, and how much the customer's own team can take on.
Failure. What happens when the system is wrong, down, slow, or abused? Which cases need a person to review them?
Baseline and evidence. Which recent examples can we look at? How does it perform now? How will we know the first version helps?
Scope and ownership. What's the smallest useful group to start with? What are we saving for later? Who runs it after launch?

Your questions should change the plan. If you end up proposing exactly what the customer asked for at the start, you probably didn't discover anything.

A worked case: support triage

This is a practice case written for this guide, not a question from any company's loop. It's the prompt from the top of the page.

Prompt: "A global software company wants an AI agent to automate support. Design and launch it in eight weeks."

Step 1: Pin down the result

Ask whether the goal is faster responses, lower cost, better answers, fewer escalations, or round-the-clock coverage. Say the customer picks a 25% cut in median resolution time, with no rise in reopened tickets.

Step 2: Map the work

Map the channels, ticket types, routing, the help content agents rely on, the actions they take, approvals, escalation, and the teams involved. Ask for recent examples: normal ones, hard ones, and ones that went wrong.

Step 3: Start small

Don't automate all of support. Pick one high-volume, low-risk ticket type where the help content is solid and every action can be undone. Start with sorting the ticket, finding the right article, and drafting a reply that a support agent approves before it goes out.

Step 4: Decide how you'll judge it

Right category and routing
Sticks to approved sources
Follows policy and tone
No unsafe or made-up claims
Effect on resolution time
Reopen and escalation rate
How often agents accept the draft, and how much they edit it
Speed and cost per ticket

Step 5: Sketch the design

Search over approved help content that only returns what each user is allowed to see. A service that runs the steps in order. One gateway that all model calls go through. Fixed rule checks for policy. A screen where agents review drafts. An audit log, a way to capture feedback, and monitoring. The AI takes no actions on its own in phase one.

Step 6: Plan the eight weeks

Week 1: discovery, baseline, and getting data and security access
Week 2: test set and design review
Weeks 3–4: prototype on past tickets, then on live ones in shadow mode, where it drafts and nobody sends
Week 5: validation, attack testing, and the user workflow
Week 6: a small production group
Weeks 7–8: measure, adjust, and decide whether to scale or stop

Step 7: Name the risks

Bad help content. Showing someone information they shouldn't see. Prompt injection, where a customer's message tricks the model into ignoring its instructions. Made-up answers. Agents trusting the drafts too much. Slow security approval. A test set that only has easy cases. And a timeline that may force a smaller scope.

Step 8: Say when you'd stop

Scale only if quality and safety hold and median resolution time improves without reopened tickets spiking. Otherwise narrow it, adjust it, or stop.

That answer shows discovery, AI system design, scope, delivery, and measurement. It also doesn't pretend eight weeks buys full automation.

Behavioral questions

Use a structure that brings out the decisions you made:

Context: the customer, the users, the system, and what was at stake.
What was unclear: what was unknown or disputed.
Your part: what you owned.
Evidence: what you saw or measured.
Decision: the options, the trade-off, and why you chose.
What you did: what you built, changed, or told people.
Result: for the system, the users, and the business.
What you learned: what became reusable, and what you'd do differently.

Have a story ready for each of these:

A customer asked for the wrong feature
You cut scope under deadline pressure
A production system failed
You learned an unfamiliar industry quickly
Security or compliance changed the design
Users didn't adopt something that technically worked
You disagreed with product, sales, or engineering
A one-off request turned out to be something many customers needed
You stopped a project because the evidence said to

Honest recovery stories beat polished ones. FDE work involves mistakes. Interviewers judge you on how fast you notice them and fix them.

Technical questions to practice

Design a data intake platform that serves many enterprise customers from one system.
Debug an integration that works in staging but fails now and then in production.
Design search across several document systems that only returns what each user is allowed to see.
Make a webhook handler safe to receive the same event twice, and easy to monitor.
Roll out a new workflow without disrupting the current one.
Design tests for an agent that takes actions in a CRM.
Protect an AI system that uses tools against prompt injection and overly broad permissions.
Decide whether a customer's request belongs in custom code, an extension, or the core product.
Make an AI workflow cheaper and faster without losing quality.
Hand off a system the customer doesn't yet know how to run.

For every design, practice explaining the customer's result and the rollout before you list any technology.

What makes interviewers wary

Solving before discovering
Treating the customer's words as complete requirements
Saying "it depends" without naming what it depends on
Designing for huge hypothetical scale while ignoring the first user
Ignoring security, permissions, day-to-day running, or adoption
Quoting model accuracy without saying on what data or task
Promising dates before understanding access and constraints
Confusing being busy with getting a result
Blaming users for low adoption
Refusing all customer-specific work on principle
Treating every customer-specific request as strategic
Describing only what the team did, never your own decisions

A two-week plan

Days 1–2: Decode the role. Read the job description, the product, customer stories, the company's engineering writing, and nearby roles. Compare the role against the FDE vs Solutions Architect comparison.
Days 3–5: Refresh production coding. Practice coding and debugging in whatever language the role actually uses. Write tests, and say out loud what happens when things fail.
Days 6–7: Practice systems design. Do two full designs, one normal enterprise system and one AI workflow. Give yourself a time limit for requirements, design, risks, rollout, and measurement.
Days 8–9: Run discovery cases. Get a friend to play an impatient customer with a broad request. Practice reshaping the problem without it turning into an argument.
Days 10–11: Build your story bank. Write eight stories with the structure above, each backed by real evidence. Only put numbers on what you can defend.
Day 12: Practice explaining to executives. Explain one project three ways: 30 seconds, three minutes, and a technical deep dive.
Day 13: Run a full mock loop. Do coding, design, a case, and behavioral back to back. Afterward, check whether you actually listened, scoped, and made decisions.
Day 14: Questions and logistics. Write your questions, rest, and confirm the format. Don't start a new technical topic the night before.

Questions to ask the company

What did the team ship for its last three customers?
What percentage of FDE code reaches the core product?
Who can turn down or reshape a customer engagement?
How are FDEs measured?
How many active builds does one person carry?
What are travel and on-call like in practice?
Who owns customer systems once they're stable?
How do FDE, solutions, product, engineering, and customer success split the work?
Why do FDEs leave this team?
Can FDEs move into core engineering or product?
What customer request did the team recently turn down, and why?

The answers tell you whether the role has real engineering behind it, or whether it's a queue of custom requests with a fancy title.

Use the career roadmap to close longer-term skill gaps, and the enterprise AI FDE guide for AI-specific prep.