OpenAI interview notes, page 61 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·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a software engineer role. The question was on payment processing and I had some real opinions about the reference solution I'd seen beforehand.

Design a payment processing system, including how you'd handle state transitions and the confirmation flow.

“I'd actually seen a walkthrough of this problem before the interview, which was a double-edged sword.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Did a 60-minute ML coding interview at OpenAI in a live notebook environment. It mixed math, coding tasks, and an open-ended research brainstorm, which is a format I hadn't really prepped for and it showed.

Implement a small numerical or ML-related task in a live Python notebook environment.
Discuss and brainstorm open-ended research ideas related to machine learning.

“The notebook format threw me more than I expected.”

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026

Coding round at OpenAI for a Software Engineer role. Five problems, I only got through three of them, and I never even saw what the last two were asking. Probably didn't pass.

Solve the 'plant infection' problem and its follow-up variations across a set of five progressively harder subproblems.

“Got through the first three parts okay but was writing in C++ and just too slow.”

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OpenAI·Software Engineer·Technical Phone Screen

Jul 2026

Got a system design/coding hybrid at OpenAI for a SWE role. The problem was building a persistent key-value store from scratch, which sounds manageable until you start thinking about recovery and what happens when the process dies.

Design and implement a persistent key-value store that supports put, get, and delete operations, and can recover previously written data after a process restart.

“My first instinct was to just keep everything in a hashmap and call it a day.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Brief OpenAI ML Engineer interview post, just one question mentioned about 1-Nearest Neighbor with the author pointing to other threads for details rather than writing it up themselves.

Explain how the 1-Nearest Neighbor algorithm works.

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jul 2026

OpenAI SWE coding round built around a single multi-part IP address iterator problem, revealed one part at a time under serious time pressure. The pacing is brutal: you don't see the next part until the current one fully passes, and they're not shy about cutting you off at the hard stop.

Implement an IPv4 iterator class that, given a starting IP address string, iterates forward from that address up to 255.255.255.255 and raises StopIteration at the boundary.
Extend the same iterator to support a reverse=True flag that walks backward down to 0.0.0.0, with StopIteration when it goes below that floor.
Add CIDR notation support so the iterator restricts its walk to the address block defined by the prefix, respecting both forward and reverse direction. The starting IP in the input may not be the network address.
Add a step parameter to the iterator so each call to __next__ advances by that integer amount instead of 1, and implement a next_batch method that returns up to N IPs from the current cursor position.

“The string manipulation approach bit me.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at OpenAI for a full-stack role. The question was a single sprawling prompt about building a ChatGPT-style product, and they really did want you to go deep on the frontend state stuff, not just wave at 'we use WebSockets and call it a day.'

Design a ChatGPT-like conversational AI product end to end, focusing on the frontend and conversation lifecycle. How does a new conversation get created, how is the sidebar title updated once the LLM has enough context, how do you handle streaming tokens to the browser, and how do you manage scroll behavior when the user is reading history while new messages are arriving?

“This is basically four questions duct-taped into one.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jul 2026

System design round at OpenAI for a software engineer role. The interviewer had actually worked on DevBox, which made it rough because they knew every corner case and pushed hard on scalability. I ran out of steam before covering all the functional requirements and the design portion wrapped up early.

Design DevBox, a cloud-based development environment system. Walk through the architecture and address scalability concerns.

“The interviewer had built this thing themselves, so every time I glossed over something they just stared at me and waited.”

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OpenAI·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026

Behavioral round at OpenAI for a SWE role. Just one question from what I can tell, cross-team collaboration stuff.

Tell me about a time you collaborated with another team.

“Picked a story about coordinating with a platform team on a shared infra dependency.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026

Hands-on PyTorch screen for an MLE role at OpenAI, two parts back-to-back in a shared code editor. Part A was debugging a broken miniGPT and adding KV caching, Part B was implementing matmul forward and backward from scratch plus a parallel scan discussion. About an hour total and it moves fast.

You're given a small decoder-only transformer that runs without crashing but produces garbage text during autoregressive generation. Find and fix the logical bug(s), then walk through your debugging process step by step.
After fixing the generation bug, implement key-value caching so that each decode step only attends over the cached prefix rather than recomputing the full sequence.
What is the memory footprint of a KV cache as context length grows, and what approaches exist to reduce it?
How does batched generation with sequences of different lengths interact with a shared KV cache and the causal mask?
Implement a custom matrix multiply op as a torch.autograd.Function, including both the forward pass and a from-scratch derivation of the backward gradients for both operands.
When A or B is broadcast in the matmul (say, one weight matrix multiplied against a batch of inputs), how does the backward change and over which axes do you sum the gradient?
Explain how a parallel prefix scan like Hillis-Steele could be applied to parallelize the tiled accumulation in matrix multiply, and be honest about when it actually helps versus when it doesn't.
Hillis-Steele is step-efficient but not work-efficient at O(n log n) work. When would a Blelloch scan be preferable and what is the tradeoff?

“This was the part that stressed me out most.”

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