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OpenAI interview notes, page 12 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·Machine Learning Engineer·Technical Phone Screen

Apr 2026
OpenAI ML engineer interview, one technical round focused entirely on implementing KV-cache generation for a transformer model. They gave you working code and asked you to make it efficient. Pretty deep dive for a single problem.
  • You're given a working transformer implementation with a basic greedy decode loop. Add KV-cache support so that autoregressive generation doesn't recompute attention over the full sequence on every step. The cache class is provided. Walk through all the changes needed and verify the outputs match the non-cached version exactly.

“This is the whole interview, basically.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at OpenAI for a software engineering role. The main problem was building an in-memory database from scratch, then extending it to talk through indexing. Pretty open-ended, which I wasn't fully prepared for.
  • Design and implement an in-memory database that supports insert and query operations, where query takes a table name, columns to project, filter conditions with operators, and optional ordering by columns.
  • How would you extend this in-memory database to support indexes, both for speeding up WHERE clause filtering and for ORDER BY?

“I started with a naive list-of-dicts approach per table and it felt too simple, so I second-guessed myself mid-explanation and started overcomplicating the schema representation.”

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OpenAI·Data Scientist·Technical Phone Screen

Apr 2026
This was a technical screen for a DS role at OpenAI, centered entirely on one meaty experiment design problem about a free-trial offer. No behavioral stuff, no SQL, just a long case that kept branching into harder sub-questions. Felt more like a product analytics take-home compressed into a live conversation.
  • For a free-trial experiment, how would you define the primary metric, key secondary metrics, and at least one guardrail metric? Give concrete definitions including exact denominators.
  • Should you use ITT or per-protocol analysis for this experiment, and how do you handle users who were assigned to treatment but never actually saw the offer?
  • Retention is only defined for users who signed up. How do you estimate the treatment effect on retention without introducing post-treatment selection bias?
  • What bugs or setup problems would you look for when reviewing Python analysis code for this experiment, without writing any code yourself?
  • How would you make the final ship/iterate/kill decision for this free-trial feature, and what additional analyses would you run to connect the results to long-term business value?

“I went with signup rate as primary, defined as unique users who started a trial divided by unique eligible visitors assigned to each variant.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
OpenAI software engineer coding round. The problem seemed manageable at first but a late-stage requirement change completely invalidated the earlier approach, and the clock ran out before anything could be debugged.
  • GPU Credit allocation problem: given a set of GPU credit transactions, implement logic to track and deduct credits, with a follow-up requirement to process deductions in end-time order.

“Started fine, passed the first few test cases, then the last two revealed a whole new constraint: deductions had to happen in end-time order.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Did a virtual onsite system design round at OpenAI for a software engineer role, focused entirely on payment system design. Pretty intense scope for a single session.
  • Design a payment system from scratch.

“There's a lot of surface area here and I think I spread myself too thin early on.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at OpenAI for a software engineer role. The whole session was basically one big question about building a chess platform, which sounds fun until you realize how many moving parts they actually want you to cover.
  • Design an online chess platform that supports real-time two-player matches. Cover matchmaking, move synchronization, server-side game validation, move history persistence, disconnect/reconnect handling with turn timers, spectator support, and post-game rating updates.
  • How would you handle game state authority and validate that moves are legal? Where does that logic live and why?
  • Walk through the data model for games and moves. What does the schema look like and what are the consistency guarantees you'd need?
  • How would you scale this to support a large number of concurrent matches?

“This was a lot to unpack in one question.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Did a system design round at OpenAI for a software engineer role. Both questions were things I'd seen floating around on forums before, but I still think I blew the system design portion.
  • Design a payment system.

“I'd actually seen this one before, which made it worse when I fumbled it.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
OpenAI SWE interview with a multi-part coding problem centered on simulating infectious disease spread across five incremental sub-questions. The problem built on itself each round, which was an interesting format I hadn't seen before.
  • Implement a multi-part simulation of infectious disease spread, broken into 5 incremental sub-questions that build on each other.

“The structure was the tricky part.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an MLE role at OpenAI and got a practical coding round where they asked me to build a key-value store from scratch. Not a LeetCode grind, more of a design-your-own-thing kind of problem which I wasn't fully prepared for.
  • Implement a simple key-value store from scratch.

“I started with a basic hash map and they kept pushing on it.”

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OpenAI·Machine Learning Engineer·Onsite - Coding / Algorithms

Apr 2026
Coding round for an ML Engineer role at OpenAI, 75 minutes, Python environment issue ate into the time a bit. The problem had three escalating test cases that pushed from a simple linear scan all the way to a hierarchical binary search, plus a verbal follow-up I fumbled through.
  • Given a sorted structure, solve a search problem that requires progressively more efficient approaches: first a linear scan, then a global binary search, then a hierarchical binary search across nested levels.

“The first two parts went fine, linear scan to binary search is pretty standard muscle memory at this point.”

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