Glean System Design & Production Engineering interview questions

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.

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 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.

Standard observability tells you the request returned 200 in 800ms. It cannot tell you the answer was wrong. The strong answer names the new failure plane LLMs introduce, and takes a real position on the prompts-and-PII question.

Prompts are production code with a worse failure mode: the regression returns 200 OK. The answer interviewers reward treats a one-word prompt edit with the same machinery as a schema migration, with one LLM-specific twist at every stage.

Rate limits protect your servers; quotas protect your business model, and most candidates design one and think they've designed both. The strong answer also covers the part customers actually feel: what hitting the limit looks like.

Enterprise deals die in this design review. The answer has three layers most candidates blur together: who you are, what you can see, and how an embedded product proves both, plus the permission-staleness trap that fails Glean loops.

Cache hits on similar prompts cut cost and latency, but one false hit serves a stranger's answer to your question. Picking the threshold and handling user-specific data is the whole game.

A 4-hour outage is ~5.6x your entire monthly error budget on a 99.9% SLA. You cannot ride it out. The design is a provider abstraction, a circuit breaker, and a tiered set of degraded modes, plus a clear line on what you cache versus what you replay.

Modulo hashing reshuffles everyone the day you add a cell. Consistent hashing fixes that and then loses to the two constraints that actually decide placement in an enterprise deployment: jurisdiction and who must not share a blast radius with whom.

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 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.

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.

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.

Under 500ms from end of speech to first audio out, across three models and a network. The trick is that you never wait for any stage to finish: you stream and pipeline everything.

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.