OpenAI Interview Questions — Page 54

OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a backend role, centered entirely on building a geo-aware points-of-interest service from scratch. Dense problem with a lot of moving parts and not a lot of hand-holding.

Design a Points-of-Interest service (think Google Places or Yelp) that supports storing location data with categories and metadata, querying nearby POIs by radius or bounding box, filtering by category or keyword, and ranking results by distance and rating. The system needs to handle planet-scale traffic with low latency, hot-spot regions, and near-real-time edits.

“This is the kind of question where you think you know where to start and then realize the scope is enormous about five minutes in.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

OpenAI ML Engineer interview with a code debugging exercise on a Transformer implementation. You get handed broken code and have to find four bugs, fix them, and explain your reasoning, then pivot to an architectural modification task. Pretty intense if you're rusty on the internals.

You are given a Transformer implementation with four planted bugs. Find all four, fix them, and explain what each bug was and why your fix is correct.
Modify the Transformer to perform a different task than it currently does. Walk through the architectural and code changes you'd make.

“This is the kind of question that sounds manageable until you're actually staring at someone else's Transformer code trying to spot subtle shape mismatches and wrong norm placements.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a software engineer role. The whole session was basically one big question about building a key-value store from scratch, and they kept pulling on threads until you ran out of things to say.

Design and implement an in-memory key-value store with get, set, and delete operations. Walk through your data structures and analyze time and space complexity.
Add TTL support to the key-value store. How would you implement lazy expiration on read, and would you also add a background sweeper?
How would you make the key-value store thread-safe? Compare a single global lock, reader-writer locks, sharded locks, and lock-free approaches.
When the store hits a capacity limit, how do you decide what to evict? Compare LRU, LFU, and TTL-driven eviction and justify your choice.

“Started with a hash map, explained O(1) average for all three ops, talked through collision handling briefly.”

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OpenAI·Product Manager·Onsite - Product Sense / Strategy

Jun 2026

PM interview at OpenAI, one question that sounds straightforward until you're actually sitting there trying to answer it without sounding like you'd just cave to whoever has the most seniority in the room.

What would cause you to push back on or delay a major product launch even when there's pressure from leadership to ship?

“I fumbled this a bit because I went straight to safety and quality issues, which felt obvious and kind of safe as an answer.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

OpenAI SWE round focused entirely on one problem: streaming entropy computation with numerical stability. Pretty niche stuff, not your typical leetcode grind. Whoever wrote up the detailed notes on this did a good job but I'd never seen this exact angle before.

Given a vector of logits, compute the entropy of the resulting softmax distribution in a numerically stable way.
Now make it streaming: logits arrive one at a time. How do you accumulate entropy online without storing all the logits?
Implement a block-wise version that computes entropy over logits processed in fixed-size chunks, using only O(1) extra space.

“The naive version blows up fast if you just exponentiate raw logits.”

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OpenAI·Data Scientist·Technical Phone Screen

Jun 2026

A/B testing case study for a Data Scientist role at OpenAI. The whole thing was structured around a free trial experiment for paid conversions, and they wanted you to walk through the full analysis from metric definition to a go/no-go call. Pretty intense for what I expected to be a more conversational round.

Calculate the paid conversion rate for both the treatment and control groups, then compute the percentage lift between them.
Run a significance test on the observed lift. State which test you're using, define your null and alternative hypotheses, and provide either a p-value or confidence interval.
Compare cancellation rates during the trial period versus after the first paid billing cycle for each group.
Estimate the net change in paid subscribers at 30 days and 60 days, accounting for both new signups and cancellations.
What additional metrics or user segments would you look at before recommending a full rollout of this feature?
Summarize the experiment results and give a clear go or no-go recommendation backed by the numbers you calculated.

“Pretty mechanical but I fumbled the lift formula for a second.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML Engineer role at OpenAI and got a coding question that looked deceptively simple on the surface. K-means with k=1 sounds like a toy problem until you realize they want you to both derive the math and verify it through iteration.

Implement a K-means clustering routine using Euclidean distance, but constrained to a single cluster (k=1). Given an array of d-dimensional points, return the centroid. You should also verify your result through iterative updates, not just the closed-form solution.

“My first instinct was to just return the coordinate-wise mean and call it done, which is technically correct for L2 with k=1.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a backend engineer role, centered entirely on building a Slack-style messaging platform. It went deep fast and covered a lot of ground in one question.

Design a team messaging platform similar to Slack, covering workspaces, channels, direct messages, real-time delivery, message history, search, threads, file uploads, and presence indicators. How do you handle millions of concurrent users with sub-200ms latency and high availability?

“This is essentially four or five hard problems stapled together and they want you to move through all of them.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a software engineer role. The problem was a crossword puzzle system and I spent a good chunk of time just figuring out what they were even asking.

Design a system to solve crossword puzzles, considering both in-memory approaches and large-scale distributed processing.

“Spent way too long on clarifying questions at the start.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

OpenAI software engineer interview focused heavily on a deep technical dive into a past project. One question, but it covered a lot of ground and the bar felt high.

Pick the most technically complex project from your resume and walk through it in depth: the business problem, constraints you were working under, the architecture you chose and what you rejected, the hardest thing you had to debug or unblock, measurable outcomes, and what you'd change looking back.

“This question is basically six questions in a trench coat.”

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