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OpenAI interview notes, page 57 of 67

Mid-sized

Most of these OpenAI write-ups come from Software Engineers (460), then Machine Learning Engineers (137) and Backend Engineers (23). The most reported rounds are the technical phone screen (335), the system design round (229), the onsite coding round (32) and the behavioral / leadership round (22).

667 interview notes · updated Jul 2026

OpenAI·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at OpenAI, one question about goal-setting and metrics for a new AI voice hardware product. Pretty open-ended and conceptual, no behavioral fluff.
  • OpenAI is launching an AirPods-style wearable with built-in voice AI. How would you define goals and success metrics for it?

“I went straight to engagement metrics and kind of forgot to anchor on what 'success' even means for a brand new hardware category.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role. The question was a deep one about building an in-memory database from scratch, covering basically every hard problem at once. Left feeling like I only really nailed two or three of the five areas they were probing.
  • Design an in-memory database that supports CRUD operations, efficient querying, transaction isolation, a persistence and backup strategy, and horizontal scalability, all within a single-node memory-resident architecture.

“This thing sprawls in every direction and I didn't pace myself well.”

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OpenAI·Software Engineer·Recruiter / HR Screen

Jun 2026
HR screen for a software engineer role at OpenAI. Pretty standard recruiter call covering the usual background stuff, nothing technically demanding, but they did ask about ambiguity which felt like a soft filter for culture fit.
  • Why are you looking to leave your current employer?
  • Are you currently employed full-time, part-time, or contracting?
  • Walk me through your promotion history at your previous roles.
  • Tell me about a project where you had to work through significant ambiguity.
  • Where else are you interviewing right now, and how far along are you in those processes?
  • What draws you to OpenAI specifically?
  • What are your compensation expectations?
  • What tech stack and problem domains do you prefer working in?

“Kept it professional.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Coding round at OpenAI for a software engineer role, pretty much one problem the whole time with a follow-up that kept expanding. The core question was path simplification but it grew into a full URL processing design discussion before I really saw it coming.
  • Given a Unix-style absolute path string, write a function to simplify it to its canonical form.
  • Given a stream of full URLs, how would you count how many times each normalized path appears, and how do you handle case sensitivity, trailing slashes, and percent-encoded segments?
  • How would your design extend to support relative paths or symlinks, and what are the complexity implications?

“Stack-based approach, pretty standard if you've seen it before.”

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OpenAI·Frontend Engineer·Technical Phone Screen

Jun 2026
Frontend coding round at OpenAI where I had to build a simplified ChatGPT-style UI from scratch, using a reference video as the spec. Finished faster than expected and spent the back half of the session just talking with the interviewer.
  • Build a simplified chat UI with an input box and submit button that renders streaming text responses from an AI backend, using a provided reference video as a visual guide.

“Knocked this out in about 35 minutes which surprised me a bit.”

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OpenAI·Software Engineer·Recruiter / HR Screen

Jun 2026
Recruiter screen for a software engineer role at OpenAI. Pretty standard stuff but the one question they pushed on caught me a little flat-footed.
  • After hearing about the role, which parts of your background map most directly to it, and where do you see gaps?

“I kind of fumbled this at first because I started with the generic 'I'm really excited about the mission' angle and the recruiter basically waited me out until I said something concrete.”

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OpenAI·Machine Learning Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for an MLE role, focused entirely on building a RAG pipeline from scratch. One long question that kept branching into follow-ups about evaluation at every layer. More to unpack than I expected.
  • Design a full RAG system end-to-end, covering data ingestion and chunking, embedding, vector store and retrieval, reranking, LLM generation, and post-processing. For each component, explain how you would evaluate its quality, including what metrics you'd use, whether evaluation is offline or online, and how you'd collect ground-truth data.

“This is one of those questions that sounds scoped until you realize every component is basically its own sub-question.”

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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 to design a remote IDE from scratch, covering everything from workspace lifecycle to tenant isolation. Pretty broad scope for a single session.
  • Design a cloud-hosted remote development environment similar to GitHub Codespaces or Replit. Walk through the full system including workspace lifecycle, file system, terminal access, language server support, port forwarding, and optionally collaborative editing.
  • How would you handle tenant isolation: VM-based versus container-based compute, and what are the security implications of each?
  • How do you design the system to keep idle costs low while still supporting many concurrent users with low cold-start latency?

“This one sprawls fast.”

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OpenAI·Machine Learning Engineer·Take-home Assignment

Jun 2026
OpenAI ML Engineer take-home where they hand you a broken Transformer implementation and tell you to find four bugs. Felt like a real debugging session more than an interview, which I actually appreciated.
  • You're given a Transformer language model implementation with four planted bugs. Find and fix all of them: a label-shifting error in the loss function, a positional embedding initialization problem, a broken causal attention mask, and a miscellaneous typo-level bug.
  • After fixing the bugs, verify your solution by training the model briefly and confirming the loss decreases and generated text looks reasonable rather than gibberish.
  • Walk through how you would systematically locate each bug: unit testing components, checking tensor shapes and numerical stability, inspecting gradients, and using a tiny dataset for sanity checks.

“The label shift one I caught fast because I've been burned by that before.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed at OpenAI for what seemed like a software engineering role. The technical portion zeroed in on complexity analysis for a simulation problem, which sounds routine until you're actually in the hot seat trying to justify every nested loop.
  • Walk through the time and space complexity of your simulation implementation, accounting for the number of steps, the data structure sizes, and any nested operations.

“I had the code in front of me and still fumbled a bit.”

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