OpenAI Interview Questions — Page 44

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI, one big open-ended question about building a distributed key-value store. They wanted the full picture, not just a sketch, so it went pretty deep.

Design a distributed key-value store. Walk through the data model and API, how you'd partition the data, your replication and consistency approach, failure handling, how you'd deal with hot keys, persistence, and how the system scales under read and write pressure.

“This is a beast of a question and I underestimated how much they wanted me to actually commit to tradeoffs rather than just list options.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Got a coding problem for an OpenAI SWE round that was basically a mini ledger system for GPU credits. The core challenge was getting the expiry logic and FIFO-by-expiration deduction right under constraints that made brute force a bit risky.

Design and implement a GPU credit ledger that supports creating grants with expiration times, subtracting credits (consuming from earliest-expiring grants first), and querying the current balance while ignoring expired grants.

“The problem sounds clean on paper but the edge cases stack up fast.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a software engineer role, basically a full Slack-from-scratch question with enterprise requirements thrown in. Brutally broad scope and I don't think I covered everything they wanted.

Design a multi-tenant team messaging platform like Slack, covering workspaces, channels, direct messages, and real-time delivery to thousands of concurrent users.
How would you handle message fan-out to potentially millions of channel members while keeping p99 delivery under 200ms?
Walk through your data model for users, workspaces, channels, and messages, including how you'd index for full-text search.
How would you design presence and typing indicators at scale without overwhelming your infrastructure?
What's your approach to ensuring message ordering and idempotency across distributed nodes?
How would you handle enterprise requirements like SSO/SAML, role-based access control, audit logging, and eDiscovery compliance exports?
Describe your multi-region strategy, including how you'd handle latency, failover, and data residency requirements.
How would you design the media storage and CDN layer for file sharing at this scale?
What rate limiting and abuse prevention mechanisms would you put in place for a platform at this scale?

“The scope was enormous and I tried to cover everything at once which was a mistake.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

OpenAI software engineer coding round, basically a credit balance system with a twist I didn't see coming until I was already halfway through a wrong implementation.

Design a credit balance system that supports add, subtract, and getBalance operations for users, where a failed subtract (due to insufficient funds) causes the next getBalance call to return None instead of the actual balance.

“I had the basic balance tracking done in like five minutes and thought I was cruising.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Did a coding round for a Software Engineer role at OpenAI. IPv4 themed problem, nothing too wild, but worth knowing your networking basics going in.

Solve a coding problem involving IPv4 address parsing or validation.

“Pretty standard stuff.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

OpenAI systems interview, one meaty design question about building a memory allocator from scratch. The kind of problem that sounds manageable until you're actually in it trying to explain coalescing out loud.

Design and implement a memory allocator with allocate(size) and free(ptr) over a fixed-size memory region. Walk through your choice of data structures, allocation policy, block splitting, and coalescing. What are the trade-offs?

“I started with the naive linked list of free blocks and they let me talk for a bit before asking about performance.”

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OpenAI·Machine Learning Engineer·Onsite - Coding / Algorithms

Jun 2026

Multi-part ML coding round at OpenAI for an MLE role, all centered on cleaning a human-annotation dataset. The problem built up in stages, each one asking for a more sophisticated filtering approach than the last. Solid problem if you've worked with noisy label pipelines before, rough if you haven't.

Given a dataset where each item has been labeled by multiple annotators, implement a majority-vote filter to remove low-quality annotations and return a cleaned dataset.
Extend your solution to compute a per-annotator agreement rate against their peers, and use that to down-weight or exclude annotators who consistently disagree with the group.
Add confidence scores to the annotations and implement a confidence-weighted aggregation scheme to produce a cleaner final label per item.
Design a method to identify and remove annotators whose disagreement with peers consistently exceeds some threshold across the full dataset, and explain how you'd set that threshold and handle edge cases like sparse annotators.

“This part felt manageable.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

60-minute coding round for a Research Scientist role at OpenAI, entirely focused on numerical computing with NumPy. The problems escalated from a basic entropy function to numerically stable variants and then space-constrained block-wise versions. Pretty intense for a single session.

Implement an entropy function using NumPy, where entropy is defined as the sum of p * log(p) over a probability distribution.
Rewrite the entropy function to be numerically stable, avoiding issues like division by zero or log of zero in the softmax computation.
Implement a block-wise entropy computation that processes the input in chunks while using only O(1) extra space.
Combine the block-wise approach with numerical stability: implement a block-wise entropy function that is both space-efficient and numerically safe.

“Straightforward enough to start.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Got a coding round at OpenAI for a SWE role and the problem was way more involved than I expected. It was a turn-based monster battle simulator where you had to find the optimal strategy across ordering and revival decisions, basically a full search problem dressed up as a game.

Design and implement an optimal turn-based monster battle simulator where you control deployment order and revival decisions to maximize win probability across all possible battle outcomes.
How would you represent the battle state efficiently to support memoization and pruning when searching for the optimal strategy?
How would you extend your simulator to support a revival mechanism where defeated monsters can be brought back under certain conditions, and how does that change your search strategy?

“This one took me a minute to even parse.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a SWE role, centered entirely on designing a multi-tenant CI/CD pipeline. The whole session was basically one long deep dive into fault tolerance and exactly-once execution, with a side trip into build caching that the interviewer left deliberately vague.

Design a scalable, fault-tolerant CI/CD system for a multi-tenant environment that schedules and executes user-defined workflows triggered by git pushes, with real-time status updates and exactly-once job execution.
How do you guarantee a job runs exactly once even if a worker crashes mid-execution?
How would you trigger the next job in a linear workflow after the previous one completes?
How do you keep multiple tenants isolated from each other in terms of resource usage, data access, and fairness?
How would you implement real-time log streaming so users can watch their job output as it runs?
The system currently has a naive CI/CD pipeline. How would you extend it to add a build cache so unchanged steps are skipped?
How would you handle passing artifacts between sequential jobs, including failure recovery for partial uploads?

“This is the main question and it's a lot.”

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