Cursor System Design & Production Engineering interview questions

A Copilot/Cursor-style assistant lives or dies on the latency budget. The strong answer spends every millisecond of a 200ms end-to-end budget on purpose, separates the inline-ghost-text path from the chat path, and shows why prefix caching and speculative decoding are the only ways to hit it at 10k concurrent users.

The instinct is to grade the codebase. Grade the feedback loop instead: a repo with no docs and a 90-second test command is friendlier to an agent than a beautifully documented one whose suite takes 40 minutes and flakes 8% of the time, and the arithmetic on that second number is worse than it looks.

Nobody turns off an AI reviewer because it missed a bug. They turn it off because it left nine comments they had to read and three that mattered. Precision is the design variable, and the arithmetic says cutting comments per PR from twelve to four costs you 7% of the real findings and two thirds of the triage time.

Every queue, webhook, and retry loop you'll ever deploy at a customer hides this distinction. Interviewers use it to separate people who've shipped from people who've read about shipping.

A staple at OpenAI and Anthropic in both coding and design rounds. Most candidates can name the algorithms; few can say which one lets 2x your limit through, and that's the part that gets scored.

The signature FDE debugging genre: a moving fault, an opaque client, and a customer who's sure it's your fault. The winning method splits the problem at the boundary, and knows the four classic culprits hiding on their side.

A Fermi estimate with a paycheck attached. The interviewers' favorite filter: candidates who jump to a dollar figure miss the two numbers that actually break deployments, peak QPS and tokens-per-minute limits.

Averages lie, and interviewers know most candidates quote them anyway. The fastest way to sound production-grade in any latency discussion is to reason about percentiles correctly, including the one trap almost everyone falls into.

This isn't a deployment question, it's a judgment question wearing one. Interviewers are scoring whether you have a decision framework, a safety checklist, and the spine to say no to a customer politely.

One malformed message can silently wedge an entire pipeline, and it happens at every customer eventually. The DLQ answer interviewers want covers what most candidates forget: what happens *after* the message lands there.

The round is conversational discovery plus live Excalidraw diagramming, and it's graded on sequence: candidates who draw before asking fail. The discovery battery, the voice-pipeline boxes worth drawing, and the latency budget that anchors the whole design.

Two questions disguised as one: classic multi-region (where the hard part is data, not compute) and dependency failover (where the hard part is that your fallback model gives different answers). Degraded modes are designed, not improvised.

A signature FDE triage question with a hidden rubric: interviewers score the *order* of your moves, not just the list. There's a reason 'what changed?' beats 'check the dashboards', and a reason mitigation beats diagnosis.

The bug only appears under load, the code 'hasn't changed,' and the customer just got charged twice. There's one root-cause pattern behind almost every version of this incident, and a fix with a subtle race most candidates miss.

The classic Palantir decomposition prompt. The winning answer hinges on one asymmetry candidates rarely surface unprompted, and on starting with something embarrassingly simpler than an ML model.

Active-active is easy until two regions write the same agent's memory at once. The strong answer picks a write model on purpose, names what conflicts and what cannot, and refuses to pretend a long-running agent run survives a regional failover for free.

The Palantir decomposition round has no right answer, it has a right METHOD, and it's the most-failed interview in the FDE loop. Here is the four-move rubric interviewers actually score: clarify, model, spine, iterate.

A reported Palantir FDSE decomposition where the fatal move is putting triage inside an algorithm. The bed-capability data model, the visibility-before-optimization v1, and the conflicting-priorities answer that keeps clinicians in charge.