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OpenAI system design interview questions

8 questions on this site are reported in OpenAI software engineer interviews. Each has a full reference design (requirements, back-of-envelope numbers, components, user flows and deep dives with interviewer follow-ups). Each one can be walked through turn by turn and then sat as a mock. Patterns covered: real-time, websockets, fan-out, chat, gpu-batching, streaming, scheduling, kv-cache, tool-calling, context-management, rate-limiting, prompt-caching, frontier, politeness, dedupe, distributed-queues, peer-to-peer, chunking, bandwidth, integrity, stragglers, rag, vector-search, hybrid-search, permissions, multi-tenancy, latency-budget, cancellation, prefix-caching, context-assembly, code-completion, ann-indexes, quantisation, segments.

Run an AI mock OpenAI interview →One of these questions, run as a live round in that style, with a debrief at the end.

Attribution comes from public interview guides and candidate reports, not from OpenAI. It indicates where a question has been asked, nothing more.

OpenAI questions

What the OpenAI round looks like

45–60 minutes, frequently on the infrastructure behind model serving, data pipelines or developer-facing APIs.

What it is reported to weigh

  • Latency and cost tradeoffs under expensive, constrained compute
  • Streaming, batching and backpressure on the request path
  • Safety, abuse and quota mechanics as part of the design, not an afterthought
  • Pragmatism: what you would actually ship first

The common mistake

Treating an AI product as a standard CRUD app. The interesting constraints are capacity, streaming and cost per request.

From public interview guides and candidate reports, and OpenAI’s own interview guidance where it publishes any. Not endorsed by OpenAI. Formats change; treat it as a prior, not a promise.

How to prepare for the OpenAI system design interview

Large-company loops run 45 to 60 minutes, expect numbers, and spend most of the time on deep dives. Walk through a question to see how a strong candidate paces the hour, sit the same question as a mock until you can produce the requirements, the estimates and the design without looking, then read the interview framework guide for how to structure the hour. The other questions on the site teach patterns that show up in OpenAI interviews under different names.

Questions people ask about the OpenAI system design interview

What system design questions does OpenAI ask?

Questions reported in OpenAI system design interviews include Slack, LLM Inference Service, ChatGPT, Web Crawler, Large File Distribution System, RAG System, GitHub Copilot and Vector Database. Between them they test real time, websockets, fan out, chat, gpu batching and streaming. The reports come from public interview guides and candidate accounts, not from OpenAI, so practise the patterns rather than memorising one list.

What is the OpenAI system design interview like?

45–60 minutes, frequently on the infrastructure behind model serving, data pipelines or developer-facing APIs.

What does OpenAI look for in a system design interview?

Reports say the OpenAI round weighs 4 things. 1. Latency and cost tradeoffs under expensive, constrained compute. 2. Streaming, batching and backpressure on the request path. 3. Safety, abuse and quota mechanics as part of the design, not an afterthought. 4. Pragmatism: what you would actually ship first. Treating an AI product as a standard CRUD app. The interesting constraints are capacity, streaming and cost per request.

How do I prepare for the OpenAI system design interview?

Practise the questions reported there out loud, under a 45-minute clock: requirements with numbers first, then estimates, a high-level design, and deep dives into the two or three hardest parts. Start with Slack: watch a strong candidate answer it turn by turn, then sit it yourself. Then cover the patterns the round leans on (real time, websockets, fan out and chat) with one more question each.