Forward deployed engineer interview questions

The candidate reports this specific design prompt. Wording is paraphrased; no official confirmation.

Clarify who shares with whom, visibility, matching behavior, and success. Model users, interests, and interactions. Walk through one user journey before discussing scale.

Follow-up to expect: What changes if interest visibility is private by default? How would you test matching quality?

The function name is reported, but the full schema and requirements are not public. Agree on them with your mock interviewer first.

Ask what results mean, whether order matters, and how prior interactions affect eligibility. Start with a correct implementation; test empty data, duplicates, missing users, and ties.

Follow-up to expect: Explain the time and space costs. Which lookup would you index, and why?

Reported as a follow-up to the same interview; the original data model is incomplete.

Identify the lookup key and read/write pattern. Compare a scan with a keyed index, explain memory and update costs, and preserve the original matching semantics.

Follow-up to expect: How do cancellations, duplicate records, or stale indexes affect correctness?

Reported follow-up; the post suggests sorting and binary search. The correct design depends on the requested matching semantics.

Clarify exact, prefix, or substring matching. Consider sorted ranges for compatible queries; account for sorting and mutation costs. Explain why prefix indexing does not solve arbitrary substring search.

Follow-up to expect: When is a simple scan better than maintaining sorted order?

The author reports repeated motivation questions in one government-track process. This is not a universal interview sequence.

Connect a specific mission or workflow to your past work. Describe the role accurately and discuss genuine fit, including work you would decline.

Follow-up to expect: What would you need to learn about the customer before accepting the role?

A paraphrase of the reported hiring-manager project discussion.

Distinguish your contribution from the team’s. Explain a decision, an obstacle, a result, and what you would change. Be precise about whether outcomes were measured.

Follow-up to expect: What evidence changed your mind during the project?

Sierra describes a plan/build/review onsite with a two-hour build. This is a published format, not a fixed question; the current recruiting process may differ.

Choose a narrow user problem, show the main flow, explain scope cuts, inspect generated code, and describe what must change before production.

Follow-up to expect: Which model-generated change did you reject, and what evidence led you there?

Sierra describes this as a debugging-interview pilot, not a confirmed universal stage. No original codebase is provided.

Reproduce the behavior, trace affected boundaries, find the smallest responsible fix, and check regressions. Explain both the code change and its user impact.

Follow-up to expect: What would you inspect before trusting an AI-generated patch?

An original interview-style question derived from a published delivery example; not reported as an actual hiring question.

Ask about the underlying blocker, deadline, existing alternatives, and acceptance criteria. Compare a workaround with a reusable feature and include maintenance cost.

Follow-up to expect: When would a workaround create unacceptable long-term risk?

An original question based on Nick Wong’s published account; not a reported Cognition interview question.

Identify the workflows, sensitive data, users, and ownership gap. Build a paved path with clear permissions, maintenance ownership, and a narrow first use case.

Follow-up to expect: What evidence would justify turning the internal capability into a customer product?

An original question derived from the published Tax AI engineering case.

Capture context and traces, distinguish genuine errors from policy changes, create representative held-out cases, and test fixes against regressions before rollout.

Follow-up to expect: How do you keep the improvement loop from overfitting to the latest corrections?

An original question grounded in the published time-to-music and customer-effort discussion.

Follow the full customer journey. Examine repeat contacts, actual task completion, cohort differences, and time to first value. Validate the data with users.

Follow-up to expect: How would you distinguish genuine resolution from a customer giving up?

An original design question based on the paper’s new-enemy problem; not a reported hiring question.

Model content and ACL versions, inherited permissions, and read consistency. Include caches and exports. State the revocation guarantee and test forbidden reads after changes.

Follow-up to expect: Where is an authorization check too stale to be acceptable?

An original question derived from the published online migration; not a Stripe interview report.

Separate write duplication, backfill, validation, read cutover, and retirement. Inspect concurrent updates, parity failures, load, and the conditions under which rollback remains possible.

Follow-up to expect: A backfill races with a newer write. Which version wins and how would you detect a regression?

An original incident discussion based on the 2018 postmortem; not a GitHub interview report.

State the data-integrity invariant, freeze unrelated changes, inspect replication, and choose a containment path. Explain the consequences of degraded service versus unsafe failback.

Follow-up to expect: Which queued jobs can be replayed safely, and which require reconciliation?

An original synthesis applying the published toil framework to FDE delivery; the scenario is hypothetical.

Use total cost per accepted task, include failures and human work, and hold task mix and quality constant. Separate realized savings from theoretical capacity.

Follow-up to expect: What changes if usage doubles but the support team cannot grow?