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OpenAI interview notes, page 5 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·Technical Phone Screen

Apr 2026
Interviewed for a software engineering role at OpenAI. The coding question was a twist on IP address manipulation, specifically extending IPv4 logic to support incrementing and decrementing addresses. Short session, not a lot of back-and-forth.
  • Implement a function that takes an IPv4 address and supports navigating to the next or previous address by incrementing or decrementing it.

“Seemed straightforward at first but the carry logic across octets tripped me up a bit.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
OpenAI MLE technical screen focused on a NumPy puzzle that escalated fast: implement 1-nearest-neighbor without loops, then rewrite it as a neural network forward pass in Wx+b form. The interviewer drilled tensor shapes at every step and paired it with a transformer bug-hunt in the same session.
  • Implement 1-nearest-neighbor classification in pure NumPy with no for-loops, given a training set, labels, and one or more query points.
  • Rewrite the 1-NN forward pass as a neural network layer in the form Y = Wx + b with an activation function.
  • Why use squared Euclidean distance instead of plain Euclidean distance, and what happens to the query norm term in the network formulation?
  • Why doesn't applying softmax change which class gets selected as the nearest neighbor?
  • Can you express 1-NN under L1 (Manhattan) distance as a single affine layer plus activation, the same way you did for L2?
  • If you need class probability scores instead of a hard label, how would you extend the network output using the training labels?

“The vectorization itself wasn't the hard part.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at OpenAI for a SWE role, centered entirely on architecting a GPT-3 Playground from scratch. Pretty open-ended, which I wasn't fully prepared for.
  • Design the high-level architecture for a GPT-3 Playground website from scratch, covering backend data organization, frontend state management, streaming responses, parameter controls, and saved presets.
  • How would you organize and model the backend data for this system, including prompts, model parameters, and user-saved presets?
  • How would you handle streaming the GPT-3 model response to the frontend in real time?
  • How would you manage UI state and handle user interactions like changing model parameters and submitting prompts?

“This was the whole interview, basically one giant open-ended prompt.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
Data science interview at OpenAI, one question that sounds deceptively simple until you're actually sitting there trying to answer it.
  • You shipped a new feature and user behavior shifted as a result. How do you define what success looks like?

“I went straight to engagement metrics and the interviewer just kind of waited, like they wanted more.”

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OpenAI·Backend Engineer·Technical Phone Screen

Apr 2026
Interviewed for a backend engineer role at OpenAI and got hit with a question that felt more like a product safety review than anything engineering-adjacent. Not what I prepped for, but in retrospect it makes sense given what they're building.
  • Walk through how you'd decide whether a new model version is ready to ship. What criteria matter, how do you weigh them against each other, and how do you structure the final go/no-go with stakeholders?

“This one threw me because I came in thinking backend infra, not model release process.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
OpenAI SWE coding round, one meaty design-your-own-cache problem that took up the whole session. More open-ended than I expected, with optional extensions that I wasn't sure whether to tackle or skip.
  • Implement an in-memory TTL cache with per-key expiration. The cache should support set, get, and delete operations, where get returns None for expired keys. Optional extensions include LRU eviction with a capacity limit, a cleanup method to proactively purge expired entries, and thread safety.

“I started with the basic TTL logic, storing expiry timestamps alongside values, and that part went fine.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Apr 2026
OpenAI backend interview that went deep on database internals. They asked me to design a mini SQL engine from scratch, which sounds like a system design question but quickly turned into a very low-level implementation discussion about parsing, execution, and storage.
  • Design a simplified SQL execution engine that supports CREATE TABLE, INSERT, and SELECT with WHERE, ORDER BY, GROUP BY, and basic aggregates like COUNT, SUM, AVG, MIN, and MAX. Walk through how you'd implement tokenization, parsing into an AST, a logical plan, and execution.
  • How would you design the in-memory storage layout for this engine, and what kind of simple indexing would you support?
  • How would you extend this engine to support JOIN operations?

“I started with the high-level pipeline and they kept pushing me down each layer.”

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OpenAI·Machine Learning Engineer·Recruiter / HR Screen

Apr 2026
Initial recruiter screen for an MLE role at OpenAI. Pretty standard stuff: org overview, team fit chat, and logistics. The pipeline they described sounded intense though, two coding screens then a final round with a research presentation.
  • Can you walk me through your background and what you've been working on?
  • Looking at the different sub-teams within the org, which one do you see as the best fit for you and why?
  • Are you open to relocating to San Francisco, and can you commit to a hybrid schedule of around three days in office per week?
  • What is your expected start date if you were to receive an offer?

“Standard opener, nothing to overthink.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
OpenAI SWE interview with a pretty meaty coding problem around GPU credit management. The core question had real design depth to it and a follow-up variant that apparently gets even messier with tiers and preemption.
  • Design a GPU credit system for a stream of transactions. Implement grant(user_id, amount, timestamp, expires_at), consume(user_id, amount, timestamp), and balance(user_id, timestamp). Consume should prefer credits expiring soonest, and balance should exclude expired credits.

“This took me a minute to internalize.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Infrastructure round at OpenAI focused almost entirely on one deep system design problem. The question looked contained but kept expanding the longer we talked.
  • Design an in-memory key-value store that can recover its full state after a crash or disconnect, using a write-ahead log. Cover the on-disk log format, durability guarantees on put and delete operations, replay logic on startup, and how you'd handle partial or torn writes. Bonus: periodic snapshotting and log truncation.

“I started with the happy path and that was a mistake.”

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