Forward-Deployed Engineer: ambiguous problem → walking-skeleton MVP

Palantir-origin format now run at OpenAI ("The Deployment Company"), Anthropic Applied AI, and Google's 2026 FDE loops. You get an ambiguous customer problem — "reduce 911 response time," "help this hospital triage faster," "this enterprise wants to deploy our model" — and 45–60 min to decompose it into a sequenced plan. Tests product judgment + technical scoping + customer empathy at once.

What this round actually is — and what it is not

Most coding interviews give you a well-specified problem: the inputs, the outputs, and the success condition are all handed to you, and your job is to write correct code. The decomposition round deliberately removes that specification. You are given a one-sentence business goal that is ambiguous (it could mean several different things) and under-scoped (the boundaries of the work are undefined). The whole exercise is watching how you convert "make 911 faster" into a concrete, ordered engineering plan — that conversion is "decomposition." To decompose a problem is to split one large, vague objective into a small set of named pieces (a metric to chase, the data you need, the interfaces between systems, the edge cases that will bite you, and a build order). Nobody expects working code in this hour; they expect a plan a teammate could pick up on Monday.

It helps to know the vocabulary the round leans on, because interviewers use these words as shorthand:

• Scope — the explicit boundary of what you will and will not build. "Clarifying scope" means asking enough questions to draw that boundary instead of guessing at it. An answer that quietly assumes the whole problem (build everything, for everyone) signals weak judgment.
• Interface — the agreed shape of how two parts talk to each other: the input a component takes and the output it returns. Naming interfaces early ("the classifier takes a call transcript and returns a ranked list of three triage codes") lets you reason about pieces independently.
• Edge case — an input or situation outside the normal happy path that can break or embarrass the system: empty data, a non-English caller, a model that is unsure, a network outage mid-call. FDE work lives in edge cases because real customers hit them on day one.
• Tradeoff — admitting that any choice gives up something to gain something else (faster but less accurate, cheaper but slower to build). Stating tradeoffs out loud is how you prove the choice was deliberate, not accidental.
• Sequencing / MVP — the order in which you build, and the smallest first deliverable that proves real value. "Sequence by risk" means building the riskiest unknown first so you learn cheaply.
• Human in the loop — a person who reviews or approves the system's output before it acts. Naming this person shows you understand that a real workflow has accountability, not just a model.

A worked decomposition, end to end

Theory is easy to nod along to and hard to reproduce under pressure, so here is the framework actually applied to one ambiguous prompt — the kind of out-loud reasoning that earns a pass. The brief: "This 911 dispatch center wants AI to reduce response time." Watch how each step narrows the fog.

# STEP 1 — CLARIFY (ask, do not architect). Out loud:
#   "Where does the time actually go today — call intake, triage, or crew dispatch?"
#   "Who picks up the call — one dispatcher, or a queue? What tools are on their screen now?"
#   "What counts as success — seconds saved per call, or fewer mis-routed crews?"
#   "What can go wrong that you'd never tolerate?" (e.g. a missed cardiac arrest)
# Answer comes back: triage is the bottleneck — deciding call severity eats ~40s.

# STEP 2 — NAME THE METRIC. Pin the vague goal to a number:
#   target = median time-to-triage drops from 40s to under 30s,
#            with mis-triage rate no worse than today's baseline.

# STEP 3 — MAP STAKEHOLDERS (each needs a different message):
#   dispatcher  -> "this drafts a suggestion; you stay in control" (reduce fear)
#   shift lead  -> "we measure mis-triage so safety can't silently drop" (skeptic)
#   director    -> "median triage time, on a pilot subset, weekly" (the decider)

# STEP 4 — DATA / INTERFACES / EDGE CASES (the engineering shape):
#   data:       last ~1000 calls + their logged final triage outcome (the labels)
#   interface:  classify(transcript) -> [top-3 triage codes, each w/ confidence]
#   edge cases: non-English caller, silence/garbled audio, model unsure (low conf),
#               two emergencies on one call -> always fall back to the human.

# STEP 5 — WALKING-SKELETON MVP (thin slice, every layer, 1 of each):
#   1 dispatcher, 1 station, read-only. Live transcript -> small classifier
#   -> show top-3 codes on screen -> dispatcher still types the final code.
#   Ships in ~2 weeks. It "walks": data in, model, human screen, logged outcome.

# STEP 6 — SEQUENCE BY RISK + OFF-RAMP:
#   riskiest assumption = "dispatcher trusts and uses the suggestion" -> test FIRST,
#   before any polished UI. Kill criterion: if median triage time hasn't moved by
#   week 4, or mis-triage ticks up, we stop and debug or sunset — stated up front.

Notice the shape of that answer. It never reaches for a fancy architecture; it reaches for the next concrete decision at every turn. By the end the interviewer has a build order, a number to watch, a named human who stays in control, the edge cases that would otherwise blow up week one, and an explicit "here's when we'd stop." That is a tractable problem, and making the problem tractable is the entire job.

What interviewers look for — and the pitfalls

Graders are scoring a handful of behaviors, mostly by their absence. The single biggest failure is diving into code or architecture before scoping — answering "I'd use embeddings + a vector DB" thirty seconds in, before you know who the user is or what success means. It reads as someone who builds first and asks later, which is exactly the person you cannot send to a customer. The cards below name the patterns that pass and the traps that sink the round.