Forward Deployed Engineer interview prep

Show that you want the customer side, not that you are settling for it. A strong answer names what energises you: seeing your code change how a real team works, solving messy problems with no clean spec, and owning the outcome.

Back it with evidence: a time you sat with users, debugged something on a customer call, or built a tool for a non-technical team and watched them use it.

What they look for: Genuine pull toward customers and outcomes, with a real example.

Use situation, task, action, result. Spend time on how you learned what they actually needed (watching them work, asking about their week), the trade-offs you made to ship fast, and the measurable result, such as hours saved or errors avoided.

What they look for: Empathy, speed to value, and a number at the end.

Be honest and practical. Explain what you value in being on site (faster trust, seeing the real workflow) and how you stay productive (clear weekly goals, async updates to your team). If you have constraints, say so; recruiters prefer honesty to a mismatch later.

What they look for: Realistic expectations about the job.

Tie the company's product to the kind of problems you want to solve. For Palantir, mention specific domains (government, healthcare, manufacturing) and the outcome-ownership culture. For an AI lab, mention a deployment pattern you find interesting, such as agents in regulated industries, and something you built with their models.

What they look for: Specific knowledge of the product and its customers.

Clarify first: which hospital, how long is "too long", who feels the pain (patients, nurses, administrators), and what decision someone could make differently with better information.

Break the problem down: arrival forecasting, triage, bed availability, staffing and discharge delays. Ask what data exists (admission records, bed management, staff rosters).

Propose a first deliverable: a live view of bed status and expected discharges for the bed manager, built on existing systems. Define success, such as median wait time and the share of patients seen within the target, and the next steps if it works.

What they look for: Clarifying questions, a clear breakdown, a small first deliverable and a success metric.

Turn the vague ask into a problem. Ask about their biggest costs and delays: fuel, failed deliveries, customer service volume, manual paperwork. Pick one with data available and clear money attached, for example manual processing of delivery documents.

Propose a pilot: extract fields from documents with an LLM, check them against the order system, route exceptions to a human. Agree on accuracy and hours saved as success measures before building.

What they look for: Pushing back on "use AI" and converging on a measurable pilot.

Clarify the decision: should maintenance be scheduled earlier, or parts ordered sooner? Identify data: sensor readings, maintenance logs, failure history.

Components: ingestion from machines, cleaning and labelling of past failures, a model or rules that flag risk, and an app or alert that maintenance staff actually use. Start with simple thresholds and the top failure mode, measure precision of alerts with the maintenance team, then improve.

What they look for: Starting simple, involving the end user and measuring alert quality.

Understand both goals. Usually the executive wants an outcome and the engineers worry about risk or workload. Find the version of the project that serves the executive's outcome while addressing the engineers' concerns, such as phased rollout or shared ownership. Put the agreed plan and success measures in writing and check in weekly.

What they look for: Stakeholder judgement and written alignment.

Week 1: map top contact reasons, pick two high-volume, low-risk ones (card status, statement requests). Agree on data access and security review.

Weeks 2–4: build the agent with retrieval over approved policy documents, tool calls to read-only account APIs, and a human handoff. Build an eval set from 200 real anonymised conversations.

Weeks 5–6: shadow mode, then a small live rollout. Success: accuracy on the eval set, containment rate, CSAT and zero policy violations. End with a go or no-go review.

What they look for: Realistic phasing, security awareness and pre-agreed success criteria.

Clarify what counts as a duplicate (same id, or same amount and timestamp?) and how to treat missing values. Then: parse with a tolerant reader, normalise types and time zones, drop or flag duplicates, group by customer and date, and sum. Write a couple of tests for edge cases (empty file, all duplicates, bad dates) and say how you would run it daily at scale (a scheduled job or SQL in the warehouse).

What they look for: Clarifying data rules, clean code and tests.

Use a window function to rank orders by date per customer and keep rank 1, joined with a filtered count:

SELECT o.customer_id, o.order_id, o.created_at, c.orders_30d FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY created_at DESC) AS rn FROM orders) o JOIN (SELECT customer_id, COUNT(*) AS orders_30d FROM orders WHERE created_at >= CURRENT_DATE - INTERVAL '30 days' GROUP BY customer_id) c ON c.customer_id = o.customer_id WHERE o.rn = 1;

Mention that customers with no recent orders drop out of the inner join, so use a LEFT JOIN and COALESCE if they must appear.

What they look for: Window functions and attention to edge cases.

Reproduce the bug first. Read the entry point and trace the path the failing input takes rather than reading every line. Add logging or a breakpoint at the boundary where data looks wrong, narrow down, then fix the root cause, add a test and check for the same bug elsewhere. Narrate your reasoning so the interviewer can follow.

What they look for: A calm, systematic method, talked through out loud.

Spend the first ten minutes on the README, examples and the main types or functions. Get a minimal example running, then build the feature in small steps, testing each one. Ask the interviewer questions when you are blocked; this round tests how you learn, not whether you already know the library.

What they look for: Fast learning with small verified steps.

Retry with exponential backoff and jitter on errors that can safely be retried, respect rate-limit headers, make writes idempotent with request ids, queue work so spikes do not drop data, set timeouts, and add monitoring and alerts for failure rates. Store failed items for replay.

What they look for: Production-grade reliability thinking.

Sort ranges by start time, then walk through them: if the next range starts before the current one ends, extend the end; otherwise push the current range and start a new one. That is O(n log n) for the sort. Mention edge cases: touching ranges, empty input and unsorted input. In customer terms: merging shift schedules or booking slots.

What they look for: Correct algorithm, edge cases and a real-world framing.

Ingest policy documents, chunk them, embed them and store them with metadata (country, effective date). On a question: retrieve relevant chunks filtered by the employee's country, generate an answer that cites the policy, and refuse when sources are missing. Add tools for personal data only through authenticated APIs. Evaluate on 150 real questions labelled by HR, monitor feedback and escalate sensitive topics to a human.

What they look for: Retrieval with permissions, citations, evals and escalation.

Collect 20–50 failing examples. For each, check: was the right document retrieved, was it in the prompt, did the model misread it, or is the source itself wrong? Group the failures. Fixes differ: better chunking or filters for retrieval misses, prompt changes for reasoning errors, data fixes for bad sources. Add the cases to the eval set so they stay fixed.

What they look for: Separating retrieval, generation and data problems.

The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. An MCP server exposes a system, such as a ticketing tool or a database, as tools a model can call. Build one when a customer wants several AI apps or agents to use the same internal system safely, with consistent permissions and logging, instead of custom glue for each app.

What they look for: A clear definition and a practical reason to use it.

Follow the customer's data rules: where data can be processed, retention, and whether it can train models. Use least-privilege access for tools, redact sensitive fields where possible, log access, protect against prompt injection by treating retrieved content as data, and get security review sign-off before launch.

What they look for: Security as a design input, not an afterthought.

Measure each step: speech-to-text, model time to first token, tool calls and text-to-speech. Then stream every stage, use a faster model for the first response, run tool calls in parallel or ahead of time, shorten prompts, and keep the conversation state small. Re-measure p95, not just the average.

What they look for: Measuring before optimising, and voice-specific levers.

Sample real documents across types and quality, including bad scans. Have the customer label the expected fields. Measure field-level precision and recall, plus the share of documents fully correct. Track cost and latency per document and set thresholds for automatic approval versus human review.

What they look for: Customer-labelled data and thresholds tied to workflow.

Write custom code when it unblocks a valuable customer quickly and is unlikely to repeat. Push for a product feature when you see the same need in several customers or the custom code would be expensive to maintain. Document the pattern either way and share it with product with evidence: number of customers, revenue at stake and workaround cost.

What they look for: Balancing customer speed with product leverage.

Ask what problem the feature solves and why Friday matters. Often there is a simpler way to meet the real need by Friday. Explain the risk in their terms, offer the alternative and, if they still want it, agree on what is in and out and document the trade-off.

What they look for: Curiosity before pushback, and a constructive alternative.

Own your part. Describe what went wrong (unclear scope, missed signals), how you recovered (reset expectations, delivered a smaller win) and what you do differently now, such as written success criteria at kickoff.

What they look for: Ownership and a specific lesson.

Understand their concern: security, extra workload or feeling bypassed. Meet them, show exactly what access you need and why, offer to work within their process, and give them credit for the result. Escalate only if the project is at real risk, and do it transparently.

What they look for: Turning a blocker into an ally.

Pick a real example, such as faster delivery with a manual step versus a fully automated version in six more weeks. Use their language: cost, risk, time and outcome. Recommend one option and say what would change your mind.

What they look for: Plain language and a clear recommendation.

Describe how you learned: talking to experts, reading their documents, shadowing users, writing a glossary. Show how quickly you became useful and what you delivered.

What they look for: A repeatable learning method.

Pick a project where you handled the messy parts too: unclear requirements, data access, deployment and support after launch. Show the outcome with a number and what you would do differently.

What they look for: True end-to-end ownership.