Cracking the Forward Deployed AI Engineer Interview

FDE interviews at Palantir, OpenAI, Anthropic, Databricks, and the AI-native startups are actually testing in 2026

FDE postings have grown fast, and the role now spans AI labs (OpenAI, Anthropic's Applied AI Engineer track, Cohere), Palantir's original FDSE, and vertical AI startups like Sierra, Harvey, and ElevenLabs. Job postings for the role were roughly 922 as of late 2025, a fivefold year-over-year jump, and kept climbing through 2026 as most AI companies built out customer-facing engineering teams. Hiring is now concentrated in New York, which has overtaken San Francisco as the top US market, with London also a major hub.

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Top 15 Forward-Deployed AI Engineers Interview Questions & Sample Answers

Role and fit

1. What actually distinguishes an FDE from a Solutions Architect or Sales Engineer?
An FDE owns production code inside the customer’s environment, post-sale, with no quota. A Solutions Architect proves feasibility pre-sale, often in a sandbox. A Sales Engineer supports the deal, not the deployment. Across roughly 1,000 FDE postings analyzed, the median salary landed near $174K, about 70% included equity, and none carried a sales quota, which is the cleanest signal that this is an engineering track, not a commercial one. If you can’t point to code you shipped that a customer runs in production, you’re describing an SA, not an FDE. tryexponent

2. “Why FDE and not a regular SWE role?”
This is the single most scored answer in the whole loop. Don’t say you like talking to people. Say something like: you’ve already been the person translating a messy customer requirement into a working system, and you want that to be the job description instead of a side quest. Tie it to one specific project where you owned discovery through deployment, not just the build.

3. What’s a realistic first 90 days as a new FDE?
Days 1-30: shadow customer calls, learn the product cold, ship one small visible win to build trust. Days 31-60: own a full deployment slice end to end, and build one integration piece designed to be reused, not one-off. Days 61-90: find a pattern across two or more customers and push a process or tooling change back to the core team. That last part matters because it shows you’re not just executing tickets, you’re feeding the product loop.

The decomposition round (the actual filter)

4. What is the “open-ended” or decomposition round, and why do most strong engineers fail it?
You get a vague, real-world enterprise problem with no clean spec, 45-60 minutes, and no single right answer. Interviewers aren’t grading your final architecture. They’re watching whether you clarify the actual goal before proposing a fix, name your assumptions out loud, and sequence work by risk instead of by what’s easiest to demo. This round typically has the lowest pass rate of any stage, around 40%, and the highest weight, around 30%, because most candidates over-index on algorithmic prep and skip this entirely. The failure mode is jumping straight to a technical proposal in minute one. Getperspective

5. Sample prompt: “A logistics company wants an agent that reroutes shipments using SAP data, live weather feeds, and input from 500 regional warehouse managers on different systems. You have an hour.”
Work it in this order and say each step out loud: clarify what “success” means to the customer (fewer delays? lower cost? fewer manual overrides?); map what data actually exists and who owns it, since SAP export cadence and warehouse manager input format will both be messier than stated; split the problem into ingestion/normalization, the routing logic itself, and the operator-facing interface, and sequence ingestion first because it’s the highest-risk unknown; then propose the thinnest possible walking skeleton, mocked routing logic wired to real data, just to prove the integration works, before touching the actual model. Naming a failure mode unprompted (”this breaks if warehouse input is more than a day stale”) is what separates a pass from a borderline.

Coding and system design

6. What does the coding round actually look like?
Less LeetCode, more realistic engineering: parsing a messy CSV with inconsistent quoting, building a rate limiter with per-user and global limits (a recurring favorite in AI-lab loops), implementing retry with exponential backoff and jitter for a flaky API, or standing up a small RAG pipeline over a folder of documents and then defending your chunking choice. Narrate continuously. Silence reads as stuck, even when you’re not.

7. Design a private RAG system for a healthcare customer with 50 million documents and HIPAA constraints.
Anchor on trust boundaries before architecture: what’s in the customer’s VPC versus what leaves it, how PHI gets scrubbed or tokenized before it touches an embedding call, and who has query-level access. Cover chunking and retrieval strategy, but spend equal time on the audit trail: every retrieval and generation needs to be logged and attributable for a compliance review. Then propose a walking skeleton scoped to one document category and one user role, not the full 50M corpus, and explain how you’d expand from there.

8. A customer needs sub-100ms latency for LLM-powered search, but the naive RAG flow runs 1.5 seconds. Where do you find the time?
Walk the full stack instead of guessing: embedding lookup latency, reranker cost, prompt size feeding the generation call, and whether you’re paying network round-trips you don’t need. Concrete levers: cache embeddings and hot queries, cut the reranking step or replace it with a cheaper approximate method, shrink the context you’re stuffing into the prompt, and consider whether a smaller distilled model handles this specific query pattern well enough to skip the full model entirely. Say which trade-off you’re making explicitly, since sub-100ms almost always costs you some accuracy headroom.

AI-specific technical depth

9. When do you fine-tune versus use RAG versus just prompt-engineer?
Prompt the engineer first; it’s cheapest and fastest to iterate. Reach for RAG when the failure mode is stale or missing knowledge, and the underlying facts change often. Fine-tune when the failure mode is behavioral, wrong tone, wrong format, ignoring instructions, not a knowledge gap, and you have enough labeled examples to make it worth the training and eval overhead. If a customer asks for fine-tuning to fix a knowledge problem, that’s a scoping conversation you need to have before you build anything.

10. How do you evaluate whether an LLM system is actually working, beyond “it looks right”?
This is reportedly the differentiator question at OpenAI-style loops, specifically because hand-waving here fails candidates who are otherwise strong. Layer three things: automated checks for narrow, verifiable tasks; an LLM-as-judge with an explicit rubric for open-ended output, run against a stratified sample, not a random handful; and a live feedback loop from real usage that you’re actually monitoring, not just logging. If your answer stops at “we spot-checked some outputs,” that’s the hand-wave they’re listening for.

11. How do you guard a customer-facing agent against prompt injection?
Treat any content the model retrieves or a tool returns as untrusted input, never as an instruction. Separate the system prompt and user-authorized instructions from retrieved or tool-returned content structurally, not just by convention, and constrain what actions the agent can take without a confirmation step for anything destructive or high-stakes. Log and alert on anomalous tool-call patterns. Say plainly that no guardrail is airtight, and that’s why action scoping and human-in-the-loop checkpoints matter more than any single filter.

12. Hosted API versus self-hosted open-weights, how do you make that call for an enterprise customer?
Hosted wins on speed to deploy, model quality, and lower ops burden, but loses on data residency guarantees and per-token cost at scale. Self-hosted wins when the customer’s compliance regime won’t allow data leaving their environment, or when volume makes the unit economics flip. The honest answer names both the customer’s actual constraint (usually compliance, sometimes cost) and the maintenance burden you’re taking on with self-hosting, since customers frequently underestimate that second part.

Client-facing and delivery judgment

13. The deployment slipped three weeks and the customer’s CTO is on the call. What do you say?
Lead with the delay, the reason in one sentence, and a revised date you’re confident in, in that order. Don’t bury it in caveats. Then offer what you can ship in the interim if anything is usable partial progress. The failure mode interviewers are watching for is over-explaining before delivering the actual news, which reads as avoidance.

14. Tell me about a time you disagreed with a customer and held the line.
Structure it as: what they were right about first, then the principle you wouldn’t compromise on and why, then how you kept the relationship intact anyway, usually by offering an alternative that solved their real underlying need. A story where you just said no and nothing else lands as rigid, not principled.

15. The customer’s security team won’t grant you production credentials. How do you unblock yourself?
Name the actual blocker to your point of contact directly instead of working around it quietly, propose a scoped, time-boxed, logged access path that satisfies their control requirements, and keep building against anything you can access in parallel so you’re not fully stalled. Silently waiting or silently working around security policy are both wrong answers here.