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OpenAI interview notes, page 59 of 67

Mid-sized

Most of these OpenAI write-ups come from Software Engineers (460), then Machine Learning Engineers (137) and Backend Engineers (23). The most reported rounds are the technical phone screen (335), the system design round (229), the onsite coding round (32) and the behavioral / leadership round (22).

667 interview notes · updated Jul 2026

OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Got a follow-up question during what seemed like a system design round at OpenAI, focused specifically on concurrency edge cases. Pretty narrow topic but it went deep fast.
  • How would you handle concurrent requests hitting the same node in a distributed system, where shared state could cause conflicts?

“This tripped me up a bit because my first instinct was to think about caching layers before I even got to the actual problem.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Interviewed for a software engineer role at OpenAI and got hit with the classic 'design a system like ChatGPT' prompt. Pretty much what you'd expect from them, no real surprises.
  • Design an AI chatbot system similar to ChatGPT.

“Classic question but still hard to nail in 45 minutes.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineering role at OpenAI and got hit with the merge intervals problem. Pretty standard algorithmic coding round, nothing too wild, but the pressure of the company name definitely made my brain move slower than usual.
  • Given a list of intervals, merge all overlapping ones and return a list of non-overlapping intervals covering the full input range.

“Knew this problem but still fumbled the sorting step at first.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role, focused entirely on building a real-time chess platform from scratch. Dense question with a lot of moving parts and I don't think I covered everything as cleanly as I wanted.
  • Design a real-time online chess platform similar to Chess.com, covering user accounts and ratings, skill-based matchmaking, real-time gameplay with move validation, per-move timers with time budgets and increments, and game history storage.

“This is one of those questions where the scope is so wide you can easily spend 20 minutes on matchmaking and then realize you haven't touched clocks at all.”

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OpenAI·Software Engineer·Take-home Assignment

Jun 2026
Take-home style system design task for an SWE role at OpenAI, basically build a mini ChatGPT clone with auth and streaming. The deep dive questions afterward are where it gets interesting, lots of 'why did you pick that' energy.
  • Why did you choose Server-Sent Events over WebSockets (or vice versa) to implement streaming AI responses?
  • How would you integrate with a Chat Completion API and handle the streaming response correctly on the frontend?
  • How do you manage user credentials securely without a backend database?
  • How do you manage chat state entirely in the browser, and what are the tradeoffs of that approach?
  • What does your error handling strategy look like if the network drops or the API fails mid-stream?

“This is the one I actually had a real opinion on.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Interviewed at OpenAI for a software engineering role and got a system design question on distributed rate limiting. Didn't make it through.
  • Design a distributed rate limiting system.

“This is the kind of question where you think you know it until you're actually in it.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role. The prompt was to design a cloud-based IDE sandbox, and the conversation went pretty deep into execution internals inside the VM. Felt okay on the high-level design but got a bit shaky when pressed on the lower-level stuff.
  • Design a cloud-based IDE sandbox that can execute user-submitted code.

“Spent most of the time on the workflow: how a user submits code, how it gets routed to a VM, results coming back.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
OpenAI software engineer interview with a tree serialization follow-up that tripped me up more than I expected.
  • Given a binary tree, serialize it into a string representation like `1(2(4,5),3(6))` using the same recursive coordination approach as the previous sum problem.

“The tricky part was adapting the recursive pattern from returning a number to building a string.”

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OpenAI·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026
Coding round for an ML Engineer role at OpenAI. The whole thing was a two-stage design problem around building a resumable iterator, and you were expected to write tests before writing any implementation. Felt more like a software engineering interview than anything ML-specific.
  • Design a resumable iterator over an in-memory list. It should support checking for remaining elements, returning the next element, saving its current position as a portable checkpoint, and reconstructing from that checkpoint so iteration picks up exactly where it left off.
  • Now extend the same save/resume contract to a file-based iterator that streams lines one at a time. The checkpoint must work across process restarts and must not load the whole file into memory.
  • Write the test suite before implementing anything. Tests must cover resuming from the start, from the middle, from the end, a full round-trip checkpoint-and-reconstruct, and a case asserting that calling save_state() does not mutate the original iterator.

“The checkpoint-not-being-a-live-reference thing tripped me up at first.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
OpenAI SWE interview with a heavy focus on PyTorch autograd internals, covering in-place vs out-of-place ops, hand-written backward passes, and parallel prefix scan. The questions were genuinely hard and the kind of thing that exposes whether you've actually read the docs or just skimmed a tutorial.
  • Implement matrix multiplication in-place, then explain why it breaks PyTorch's autograd.
  • Now implement the same matrix multiplication out-of-place so that autograd works correctly.
  • Write the backward pass for matrix multiplication by hand, without relying on PyTorch's autograd to do it for you.
  • Implement Hillis-Steele parallel prefix scan for the forward pass, then derive and implement its backward pass.

“The interviewer ran my code and I got a cryptic version-counter error.”

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