OpenAI Interview Questions — Page 58

OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Project deep-dive round at OpenAI for an iOS Engineer role. The whole thing was one long conversation about a single past project, and they went surprisingly deep on every layer.

Walk me through a significant project you've owned end-to-end: the problem it solved, your specific role, the key design decisions you made, the technical challenges you hit, and what the actual impact was.

“This is the whole round basically.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a Software Engineer role at OpenAI, coding round with a heap-based problem. Nervous going in but the interviewer was relaxed and easy to work with, and it ended up going better than expected.

Solve the GPU Credit II problem (a heap-based coding problem).

“Embarrassed myself right away by trying to import heapq from collections, which is just wrong.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Infrastructure Engineer interview at OpenAI that centered almost entirely on building a mini query engine from scratch. Pretty design-heavy for what I expected to be a coding round, and the discussion kept expanding into territory I wasn't fully prepared for.

Design and implement a simple in-memory SQL-like query engine that supports filtering records by fields like name, age range, and ID. Walk through your data model, how you represent predicates, query execution strategy, and how you'd extend it with AND/OR logic, sorting, and projection.

“This one sprawled way more than I expected.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a software engineer role. The prompt was scoped to a coffee-shop POS rather than some abstract distributed payments platform, which I actually appreciated. Covered a lot of ground fast.

Design the ordering and payment system for a coffee-shop chain, covering both mobile app ordering and in-store register flows.
How would you model the data for orders that include modifiers like size, milk type, and syrups?
Walk through how you'd decompose this into services, for example order service, payment service, kitchen display, inventory, and loyalty.
How would you handle payment retries and ensure a customer isn't charged twice if a request fails partway through?
How would you design the loyalty and rewards integration, including earning and redeeming points as part of the checkout flow?
What would your approach be for aggregating sales and popular item reporting across all stores?

“I started with the mobile vs in-store split and that turned out to be the right move.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for an infra role, focused entirely on building a GitHub Actions-style CI/CD platform from scratch. Pretty grueling scope for a single session and I came out feeling like I'd only scratched the surface on half the topics.

Design a multi-tenant CI/CD platform similar to GitHub Actions at scale. Walk through the workflow definition model, how jobs form a DAG, runner pool architecture, scheduling fairness, log streaming, artifact caching, secrets management, retries, timeouts, and observability.

“This was basically the entire interview compressed into one prompt.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

OpenAI ML interview that went deep on noisy label problems, which I wasn't fully prepped for. The question covered a lot of ground and felt more like a mini case study than a single question.

You have a text dataset for binary classification where multiple annotators have labeled each example and their labels frequently disagree. How would you analyze the dataset, aggregate the noisy labels, train a model, and evaluate whether your improvements are real?

“This one sprawled in a way I didn't expect.”

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

OpenAI Research Engineer coding round. One meaty OOP design problem that started reasonable and then kept growing with follow-ups until I was basically writing a small game engine.

Design classes for a monster battle simulation. You have two teams of monsters that fight in order. Return the winning team and a log of the battle.
Extend the simulation to support type weaknesses and resistances between monsters.
Now give each monster multiple attacks, and each monster should automatically pick the attack that deals the most damage to its current opponent.

“The core design part was fine, Team, Monster, Outcome classes aren't that hard to sketch out.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

OpenAI SWE interview where they dropped a broken Transformer implementation on me and said find four bugs. No hints, no warm-up, just here's the code, good luck.

You are given a Transformer implementation with exactly four bugs. Identify and fix all of them, explaining each fix. Bugs may include shape or dimension errors, masking errors, missing scaling factors, softmax applied along the wrong axis, parameter initialization issues, or subtle variable name typos.

“This wrecked me a little.”

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OpenAI·Frontend Engineer·Onsite - System Design / Architecture

Jun 2026

System design round for a frontend-leaning role at OpenAI, centered entirely on building an AI prompt playground with streaming, presets, and failure handling end to end. The scope was bigger than I expected and the interviewer wanted me to drive, not just answer.

Design a web-based AI prompt playground (similar to the ChatGPT Playground) where users can type a prompt, configure model parameters, run it, and watch the response stream back token by token.
Walk through the full client-to-provider data flow for a single run, including how streaming tokens get from the model provider back to the browser.
How would you design the model gateway to abstract away provider-specific details like API keys, parameter ranges, and streaming wire formats?
What failure modes do you need to handle across the run lifecycle, covering both your own server and the model provider, and what should the client do in each case?
Is it always safe to retry a failed run? What changes if the first token has already been streamed to the user before the failure?
A user reloads the page mid-generation. How do you let them reconnect to an in-flight run instead of losing it?
You're adding a second model provider with a different streaming format and parameter limits. What changes in your architecture and what stays the same?
How do you enforce per-user and per-org cost and rate limits without adding latency to time-to-first-token on the hot path?
The model output is rendered as markdown or HTML in the browser. How do you defend against prompt-injected content causing XSS, especially while streaming partial markup?

“This was the core question and it ate the whole session.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a backend engineer role, focused entirely on building a cloud-based IDE from scratch. The scope was massive and I felt like I was playing catch-up the whole time trying to cover everything they wanted.

Design a cloud-based IDE with features like multi-file editing, syntax highlighting, autocomplete, sandboxed code execution, terminal access, and real-time collaboration.
How would you handle scaling a large number of idle user sessions without burning through infrastructure costs?
Walk me through how you'd implement real-time collaborative editing across multiple users in the same file.
What are the security and isolation requirements for running arbitrary user code, and how would your architecture enforce them?
How would you keep keystroke latency low in the editor given that some operations like autocomplete depend on a remote language server?

“This is a monster of a question.”

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