OpenAI Interview Questions — Page 27

OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

OpenAI system design round focused entirely on building a distributed cache from scratch. It was one of those interviews where the scope keeps expanding and you realize halfway through that you're not going to cover everything.

Design a distributed caching system similar to Redis or Memcached, covering the full stack from API design to failure handling.
How would you handle hot keys in a distributed cache, and what are the tradeoffs of different mitigation strategies?
Compare LRU, LFU, and TinyLFU eviction policies. When would you choose one over the others?
What are the tradeoffs between client-side routing and using a proxy layer for cache access?
How do you handle node additions and removals in a consistent hashing setup without causing massive cache invalidation?
What observability metrics would you instrument in a distributed cache, and how would you use them to diagnose problems?
When would you choose strong consistency over eventual consistency in a caching layer, and what does that cost you?

“This is basically the whole interview in one question.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

OpenAI ML Engineer interview that went about as well as you'd expect when you run out of time on a coding problem. Two-part simulation question that started reasonable and escalated fast, and I didn't make it through everything they had planned.

Given an M by N grid where each cell is either infected (X) or susceptible (*), simulate the spread of infection day by day. A susceptible cell becomes infected if at least K of its 8 neighbors are infected. Return the number of days until the grid stops changing.
Extend the simulation: after a cell has been infected for D days, it becomes immune (I) on day D+1. Immune cells can't be infected and don't spread infection. Now return the number of days until herd immunity, meaning no infected cells remain.

“Got through this part okay.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role. The core problem was building a social graph with snapshot/versioning support, which sounds manageable until you actually have to think through the space complexity tradeoffs.

Design a social network data structure that supports follow, unfollow, snapshot creation, and querying whether user A was following user B at a specific past snapshot. Discuss time and space complexity tradeoffs.

“My first instinct was to just copy the whole graph on every snap() call and I said it out loud before catching myself, which was a rough start.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a SWE role at OpenAI and got a graph/topology problem. Pretty classic dependency graph territory but the open-ended framing made it trickier than expected.

Given a set of machines and their connections or dependencies represented as a graph, solve a topology-related problem such as detecting cycles, producing a valid ordering, answering reachability queries, or modeling failure propagation.

“The prompt was deliberately vague at first, which threw me off more than the actual coding.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a software engineer role at OpenAI and got a grid simulation problem that looked deceptively approachable at first. The edge cases are where it gets messy and I definitely underestimated how much the recovery mechanic would complicate things.

You're given a 2D grid where cells are either infected, healthy, or immune. Each day, healthy cells adjacent to infected ones become infected. Infected cells recover after exactly D days and can be reinfected later. Immune cells never change. Return the first day where nothing changes between consecutive days.

“I spent the first few minutes just staring at the recovery rule because D=0 is a weird edge case where cells infect and recover in the same step.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for an EM role, basically a deep dive into building something like Slack from scratch. They pushed hard on trade-offs and weren't satisfied with surface-level answers.

Design a large-scale messaging system like Slack, starting with direct messages and then extending to channels of varying sizes.
How would you handle message delivery differently for small versus very large channels, and what are the trade-offs between push and pull models?
How would you design the inbox and notification fanout, especially for users who belong to a large number of channels?
How would you track online presence and support multi-device scenarios, including per-device read state?
Walk through your approach to message storage, history retrieval, caching, and sharding for a globally distributed messaging system.

“This took up most of the session.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

OpenAI ML Engineer interview with a grid simulation problem that built on three prior questions. The final part added a death mechanic and asked for a tuple return, which honestly felt like the interviewers were just stacking complexity to see where you'd crack.

Given a grid where cells can be infected, if any non-'I' cell has at least K infected neighbors, it dies after D days. Building on the previous parts of this problem, return a tuple containing the total number of days until the grid fully stabilizes (no infected cells remain) and the total death count.

“This was the fourth layer on the same grid problem and by this point my solution was getting messy.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

ML engineer screen at OpenAI, just the one question from what I can tell. Short and technical, felt more like a warmup than a deep dive.

If your feature set contains highly correlated features, how do you decide which ones to keep when building a model?

“I talked through a few angles: checking variance inflation, dropping one of a correlated pair based on domain knowledge, or just letting regularization handle it.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI SWE interview with a distributed systems design problem. The question was pretty niche and required designing a full async message protocol from scratch, which I wasn't expecting to go that deep on.

Given a distributed tree where nodes can only communicate via async messages (sendAsyncMessage and receiveMessage), design a protocol so that the root node can reconstruct and print the full topology as a string like 1(2(4,5),3(6)). You need to handle concurrent message arrivals and distinguish request from response messages.

“This one took me a while to even get my head around.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI SWE interview with a multi-part simulation problem centered on modeling plant infection spread across a farm field. The question built up incrementally across five parts, each layering on new rules or metrics. Pretty intense for a single coding round.

Implement a multi-part plant infection spread simulator over a farm grid, where each part adds new rules or metrics such as state updates, neighborhood spread logic, infection counts, peak statistics, time-step queries, and mechanics like immunity, recovery, or barriers.

“Five parts back to back, each one depending on your previous code being clean enough to extend.”

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