The forward-deployed engineer interview guide

A forward-deployed engineer (FDE) embeds inside a customer's organization and writes production code against their real data and systems, then owns whether it delivers a result. Unlike a product engineer who is measured on shipping a feature, an FDE is measured on whether the customer actually gets the promised outcome. If the software works but nobody adopts it, the FDE has failed.

Three things held at once: whether you can build production code, embed in a customer's real systems and constraints, and own an outcome in production. In practice that spans software fundamentals, the modern AI stack (LLM apps, agents, RAG, voice), systems integration, evals and reliability, security posture, and the non-code deployment craft of discovery, scoping, and adoption.

Ship one system end to end and be ready to describe where it broke in production. Pick two technical areas and go deep enough to know the edge of your knowledge. Prepare an outcome story that is not about code, usually a deployment that stalled on adoption or bad data. Know your numbers: latency budget, cost per request, eval pass rate. Practice reading unfamiliar code out loud, since live debugging in a repo you did not write is a common exercise.

Expect scenario questions that force a demo-versus-production judgment rather than trivia. Representative examples: debug a slow endpoint in a service you have never seen; an agent calls a tool that charges a customer, make that safe; a RAG system returns confident wrong answers, diagnose it; the data cannot leave the customer's VPC, redesign your stack; the pilot works but nobody uses it, what did you miss. Coding rounds usually happen in an unfamiliar codebase rather than on a whiteboard.

A standard loop tests whether you can build to a spec. An FDE loop adds two dimensions: whether you can work inside someone else's systems, data, and constraints, and whether you will own the outcome after it ships. That means more integration and reliability questions, real ambiguity instead of clean problem statements, and at least one round on stakeholder discovery and adoption that has no code in it at all.

You need to be a strong engineer first; the AI layer is learnable and moves fast. The strongest candidates pair solid software fundamentals with genuine depth in a few areas of the AI stack (agents, RAG, evals, or voice) and the judgment to integrate them into messy real systems. Nobody is expert across all of it, but you should be able to go deep on demand in any one area.

Senior. Only about 3% of open FDE roles are early-career, because the job requires making architecture decisions alone, working in someone else's codebase, and owning outcomes you only partly control. Median total compensation is around $185,000, rising past $250,000 at the frontier AI labs. The Plank AI Accelerator is the deliberate on-ramp for early-career engineers into exactly this bar.