OpenAI Interview Questions — Page 29

OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role, focused entirely on payment processing. The interviewer went deep on specifics rather than high-level architecture, which I wasn't fully prepared for.

How would you design a payment processing system end-to-end?
How do you compute and enforce idempotency keys in a payment system? Walk through the fields, hashing strategy, TTL, and how enforcement works across the full request lifecycle.
What are the trade-offs between an upsert model and an append-only ledger for storing payment records? Which would you choose and why?
What would you define as the MVP for this payment system, and what features are explicitly out of scope for the initial version?
Define 'noisy neighbor' in the context of a multi-tenant payment system. How do you quantify its impact on availability and latency, and what mitigations would you put in place?

“I started with the usual stuff, clients hitting an API, a payment service, a database.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Got a multi-part coding problem at OpenAI for a SWE role, all centered around IPv4 addresses and CIDR blocks. Three parts, each building on the last, and by part three I was definitely sweating a little.

Implement two functions to convert an IPv4 address string to a 32-bit unsigned integer and back again.
Given a start and end IPv4 address, iterate through every address in the range in increasing order. Discuss the complexity and how you'd handle very large ranges.
Given a starting IPv4 address and a count n, return the minimum set of CIDR blocks that exactly covers those n consecutive addresses.

“This part was fine.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a software engineering role at OpenAI and got a coding question that looked deceptively simple on the surface. Basically had to build a mini in-memory database from scratch, which ended up being way more involved than I expected once edge cases started piling up.

Design and implement an in-memory database class supporting set(key, val) and get(key, sort_by=...) operations. Handle edge cases like key overwrites, missing keys, sorting on multi-field records, and stable ordering when sort values are equal. Also discuss your data structure choices and time complexity.

“I started with a plain dict and felt good about it for about three minutes.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Phone screen for a software engineer role at OpenAI, focused entirely on system design for a payment system. Pretty standard format, one question, one shot.

Design a payment system.

“This is the kind of question where you think you know where to start and then realize mid-sentence you haven't thought about idempotency or failure states at all.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

Technical screen for an ML Engineer role at OpenAI, heavy on NumPy internals and numerical computing. The whole thing felt like a deep dive into whether you actually understand what's happening under the hood, not just whether you can call the right functions.

Write vectorized NumPy code to compute pairwise cosine similarity between two matrices X (shape n×d) and Y (shape m×d) without using any Python loops. Analyze the time and space complexity of your solution.
Implement a numerically stable softmax for a 2D array along the last axis using NumPy. Why does numerical stability matter here and what pitfall does your implementation avoid?
If X has shape (n, 1, d) and Y has shape (1, m, d), explain how NumPy broadcasting works when you compute operations between them. What is the resulting shape and what are the memory implications?

“I knew the formula cold but fumbled the normalization step at first.”

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OpenAI·Software Engineer·Hiring Manager Screen

May 2026

Interviewed for a SWE role at OpenAI, just one question about what my day-to-day actually looks like as an engineer. Pretty low-key but I still managed to overthink it.

Walk me through what a typical day looks like for you as a software engineer.

“I rambled.”

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OpenAI·AI Engineer·Technical Phone Screen

May 2026

OpenAI AI Engineer interview with a multi-part coding problem centered on matrix multiplication and automatic differentiation. The whole thing built progressively, each part depending on the last, which made it feel more like a gauntlet than a standard coding screen.

Implement an in-place matrix multiplication routine for a sequence of square matrices, then explain why this approach breaks backpropagation.
Rewrite the matrix multiplication chain as an out-of-place function that is compatible with autograd.
Manually implement the backward pass for the out-of-place matrix multiplication chain, computing gradients with respect to each input matrix without using autograd.
Implement both the forward and backward passes for the matrix chain product using a Hillis-Steele parallel prefix scan pattern, leveraging the associativity of matrix multiplication.

“The implementation itself was fine, maybe five minutes.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI software engineer coding round, one question about a social network system. Nothing too wild but I definitely hadn't drilled this one beforehand and had to figure it out live.

Design a social network with follow/unfollow functionality and a feature to recommend up to K friend-of-friend connections.

“Had seen this floating around before and never actually sat down to work through it.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Math-heavy technical screen for a Research Scientist role at OpenAI. The whole thing was basically one extended probability problem about LLM inference latency, with each follow-up drilling deeper into optimal restart strategies. Genuinely interesting but the later parts got abstract fast.

An LLM's inference time T has expected value E[T] = 1. Use Markov's inequality to upper-bound P(T > 5).
If you can restart the inference process once (restarting after a threshold time TK), what is the probability of completing within 10 minutes total?
Generalizing the above: if you can restart N times, what is the overall success probability within the total time budget?
Is there an optimal restart strategy, meaning a choice of when to restart, that maximizes the success rate?
Is there a specific distribution for T where the success rate is completely unaffected by any restart strategy?
If you can restart at varying time intervals rather than a fixed threshold, does a non-uniform restart schedule improve the success rate?

“Straightforward if you remember Markov: P(T > a) <= E[T]/a, so 1/5.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role, centered entirely on designing a chess platform from scratch. Pretty intense scope for a single session and I left unsure if I covered enough ground on the anti-cheat and global deployment pieces.

Design an online chess platform that supports real-time matchmaking, live gameplay, and spectating at scale.
How would you handle clock management across different time controls like blitz, rapid, and classical, and what happens on timeout or disconnect?
Walk me through how you'd approach server-side move validation and what signals you'd use for anti-cheat engine detection.
How would you design the rating update system, for example Elo or Glicko, and when would those updates happen relative to game completion?
How would you architect this system for global deployment to minimize latency for players in different regions?

“This is a beast of a question because the scope is deceptively wide.”

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