OpenAI Interview Questions — Page 7

OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Interviewed for a SWE role at OpenAI and got a graph-based problem centered around social network computations. Not a lot of detail in what I can share but the problem space was interesting if a bit vague on exact requirements going in.

Given a social network represented as a graph (users as nodes, relationships as edges), implement a computation on that graph. The exact task was left open but could involve things like shortest path between two users, finding mutual friends, identifying connected components, or recommending connections via common neighbors.

“The vagueness was the hardest part.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

OpenAI software engineer interview that went deep on ML infrastructure fundamentals. The main question was about embeddings and how they power search systems, which sounds approachable until you realize how many layers they expect you to cover.

Explain what an embedding is and walk through how embeddings are used in search and retrieval systems end to end.

“This started fine.”

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OpenAI·Data Scientist·Technical Phone Screen

Apr 2026

A/B testing case for OpenAI's data scientist role. The whole thing was basically one big scenario about a free trial experiment, and they pushed pretty hard on the stats and business interpretation side of things.

For a free-trial A/B test on a paid subscription product, which primary metrics would you track to measure the trial's impact on subscription behavior? Define each one precisely.
Calculate the lift in conversion to paid subscription between the treatment and control groups, then test whether the difference is statistically significant.
Estimate the treatment effect on cancellations within 30 days. Is the effect practically meaningful, not just statistically significant?
Based on your analysis, would you recommend rolling the free trial out to all users? Walk through your reasoning.

“I went with conversion rate (trial users who subscribe divided by total users assigned to treatment), churn rate within 30 days (cancels over paid conversions), and revenue per user.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Interviewed at OpenAI for a software engineering role and got a refactoring question, which sounds easy until you're actually sitting there trying to decide how much to change without breaking the spirit of the original code.

Given a working but poorly written code snippet with long functions, unclear variable names, and no tests, refactor it to improve readability, modularity, and testability. Add helper functions, rename variables, and write unit tests.

“I spent the first few minutes just reading the code and honestly second-guessing whether I should rename everything or stay conservative.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Interviewed for a Research Engineer role at OpenAI and got hit with a classic systems design problem dressed up as a coding question. The text buffer problem sounds deceptively simple until you're actually trying to justify your data structure choices out loud.

Design a data structure for a text buffer that supports insert at a position, delete over a range, and retrieving the full text. Walk through the time complexity of your chosen approach.

“I went with a list of characters in Python because I knew a rope implementation would take forever to code under pressure.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at OpenAI for a software engineer role. Just one question but it had a lot of surface area and I'm not sure I covered it well.

Design a system like chess.com, covering real-time gameplay, matchmaking, and scale.

“I started with the game state and move validation logic which felt right, but I think I underweighted the real-time communication layer.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at OpenAI for a software engineer role, focused entirely on building a payment processing service with exactly-once charging guarantees. Pretty brutal in scope, they wanted end-to-end architecture plus deep dives on idempotency and failure recovery all in one session.

Design a payment processing service that sits between a checkout flow and an external payment provider, ensuring cards are never double-charged even under retries, timeouts, and duplicate webhooks.
What clarifying questions would you ask before diving into this design, specifically around what 'exactly once' means and who is allowed to retry?
Walk through the API design and data model for a single charge, including the full lifecycle from checkout request to recorded result.
How do you design the idempotency mechanism end-to-end: key generation, storage, enforcement under concurrent duplicate requests, and propagation to the PSP call?
The PSP call times out and you don't know if the card was charged. How do you resolve the unknown outcome without risking a double charge?
How do you handle duplicate and out-of-order webhooks from the PSP, and how do downstream systems like the ledger and fulfillment service receive the final payment result exactly once?
How would you extend this design to support auth-then-capture, voids, and partial refunds while keeping idempotency intact?
The PSP goes down for 10 minutes. How do you degrade gracefully and what are the correctness implications of queuing vs failing closed vs failing over to a second PSP?
How do you detect and prevent double charges caused by a buggy client that generates a fresh idempotency key on every retry?

“This is the core prompt and it's a lot.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

OpenAI software engineer interview that centered on a grid simulation problem with a pretty nasty distributed systems follow-up. The core question wasn't too bad but the scalability angle is where things got real.

You have an N×M grid where cells can be infected, healthy, or immune. Each tick, infected cells spread to their healthy neighbors (4 or 8 directional), skipping immune ones. Implement the simulation. Variant: immunity wears off after D days.
The grid is now too large to fit in memory on one machine. How do you redesign this simulation to run at scale?

“Standard multi-source BFS and I knew it.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

OpenAI system design round for a software engineer role. The problem was dressed up as 'design Sora' but really it's a task scheduler under the hood, and if you miss that you're already behind.

Design a video generation scheduling system (like Sora): focus on what happens when compute resources are exhausted and how worker preemption is handled, including task re-queuing and user notification on completion.

“The 'design Sora' framing almost got me spending time on the ML pipeline stuff, which is a trap.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026

Got a coding round for an ML Engineer role at OpenAI and the problem was a grid simulation with a pretty nasty extension tacked on. The core mechanics weren't too bad but the optimization part at the end had me sweating.

You have an R by C grid of plants, each in one of four states: healthy, infected, recovered, or dead. Given infection thresholds T and K, a delay D, and a simulation length N, implement a day-by-day simulator where plants transition states based on how many infected neighbors they have, with all updates applied simultaneously from a snapshot taken at the start of each day.
Before the simulation begins, you can burn exactly one full row or one full column, instantly killing all plants in it. Which row or column minimizes the total number of dead plants after N days? Return the minimum dead count and the action taken, breaking ties arbitrarily.

“The simultaneous update rule is where I initially tripped up.”

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