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OpenAI interview notes, page 41 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

Jun 2026
OpenAI Research Engineer interview that dropped a buggy Transformer implementation in front of you and said 'find the problems.' There was also a follow-up where you had to convert the model into a classifier and run it against a test harness, so the code actually had to work, not just look right on a whiteboard.
  • You're given a hand-written Transformer implementation with four bugs in it. Find all four, fix them, and explain what each one was doing wrong.
  • Now convert that Transformer model into a classifier by swapping out the final layer, and make sure the code runs end-to-end against the provided test harness.

“This is the kind of question that sounds manageable until you're actually staring at someone else's code and second-guessing every line.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI, one big question that ate the whole session. The kind of problem where you think you know where it's going and then it keeps expanding.
  • Design a distributed system to fill a 50x50 crossword puzzle with 100 word slots, drawing from a dictionary of 1 million words. Show why a single machine can't do this at scale, then walk through a multi-machine solution covering task splitting, distributed backtracking, work stealing, pruning, communication overhead, and termination detection. Also compare with stochastic approaches like simulated annealing.

“This question is massive.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a backend role. The whole session was basically one deep problem about building a memory allocator from scratch, and they kept pushing on the data structure choices until I ran out of confident answers.
  • Design a memory allocator that supports allocate(size) and free(ptr). Walk through the data structures you'd use and how you'd achieve O(log n) per operation.
  • How would you handle fragmentation in your allocator design, and how does coalescing adjacent free blocks work?

“I started with a free list, which felt safe, but they pushed back almost immediately asking how I'd find a block of the right size efficiently.”

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OpenAI·Machine Learning Engineer·Onsite - Multi Round

Jun 2026
Onsite loop at OpenAI for an ML Engineer role on the research side. Two rounds back to back: one where you discuss the state of your field like you own it, and one where you present a project and get grilled on every decision. The interviewers are clearly domain experts and they will not let vague answers slide.
  • Walk us through the state of the art in your research area. What are the main families of methods, and when does each one win?
  • What hands-on experience do you have in this area? Specific models, datasets, infrastructure, and failures.
  • Where do you think your research area is heading, and what would prove you wrong?
  • How would the research directions you described translate into an actual product, given real latency, cost, and privacy constraints?
  • Present a recent research project. What was the problem, what was your contribution specifically, and why did you make the key design choices you made?
  • How did you design your experiments? Were your baselines fairly tuned, and could your result be an artifact of measurement rather than a real effect?
  • A reviewer says your result is just because the baseline was under-tuned. How do you respond, and what would you have done to prevent that criticism?
  • Your method improves a benchmark metric that's known to be gameable. How do you show the improvement is real?
  • If you had 10x the compute, or only 1/10th, how would your method and conclusions change? Which experiment would you run first to find out?
  • If you had to ship your method into a latency- and cost-constrained product tomorrow, what would you monitor online and what failure mode would you address first?

“This is the question that exposes whether you actually think about your field or just read papers.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
OpenAI interview focused entirely on designing a family of IPv4 iterators in Python. The technical depth surprised me, it went way beyond just 'implement a range' and kept pushing on memory, complexity, and edge cases I hadn't thought through.
  • Design a family of IPv4 iterators: a forward iterator starting from a given IP up to 255.255.255.255, a reverse iterator going down from a given IP to 0.0.0.0, and a CIDR-block iterator that yields every IP in a block like '192.168.1.0/24'. Walk through your implementation and discuss the time and space complexity of each.
  • How would you optimize these iterators to avoid repeated string parsing and formatting on every step?
  • What are the trade-offs between lazy generation and materializing the full IP range up front, especially when the consumer might only page through part of the iterator versus consuming it fully?
  • Should the iterator's __next__ support skipping ahead by an arbitrary delta in O(1) time? How would you implement that and what does it change about the design?

“My first instinct was to store IPs as strings throughout and I started going down that path before the interviewer raised an eyebrow.”

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
OpenAI SWE coding round, one problem the whole session. Classic grid BFS spread question, felt straightforward but the edge cases around impossibility tripped me up a bit.
  • Given an m x n grid where cells are either empty, uninfected, or infected, find the minimum number of minutes for all uninfected cells to become infected (infected cells spread to 4-directional neighbors each minute). Return -1 if it's impossible.

“Multi-source BFS, pretty standard once you recognize it.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed at OpenAI for a software engineering role and got a design question that looked clean on the surface but had some tricky state management hiding underneath.
  • Design and implement a SocialNetwork class that supports adding users, following other users, creating snapshots of the follow graph, and querying whether a user followed another user at a specific snapshot.

“The add/follow parts were fine, took maybe five minutes.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
OpenAI system design round for a software engineer role, one meaty question about building a persistent key-value store from scratch. The kind of problem that sounds manageable until you're actually in it and realize how many small decisions stack up.
  • Design and implement a persistent key-value store backed by a single file. It needs to support put, get, and delete operations, survive process restarts by reconstructing state from disk on startup, and you should define your on-disk record format and talk through how you'd handle compaction.

“I started with the in-memory HashMap part and felt fine, then they asked me to define the actual byte format and I kind of froze for a second.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
One round at OpenAI for an MLE role, focused entirely on numerical computation. Short but surprisingly specific.
  • Given a set of logits, compute the entropy of the resulting distribution. Your solution must be numerically stable.

“The entropy part is straightforward but the numerically stable piece is where it gets real.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role. The whole session was basically one giant question about building a Redis-like in-memory store from scratch, and they kept pulling on threads until I ran out of things to say.
  • Design an in-memory key-value database that supports get, set, and delete operations with O(1) average-case read and write performance. What data structures would you use?
  • How would you implement conditional updates like compare-and-set in your key-value store, and how do you ensure atomicity?
  • Walk me through how you'd implement TTL-based key expiration. What are the tradeoffs between a min-heap, a timing wheel, and lazy expiration?
  • How would you handle memory pressure and eviction? Describe how an LRU eviction policy works and how you'd implement it efficiently.
  • How would you support atomic multi-key operations, similar to a transaction across multiple keys?
  • Describe your approach to persistence: how would you design an append-only log combined with periodic snapshots, and what are the tradeoffs?
  • How would you design crash recovery for this system, and what consistency guarantees can you offer after a restart?
  • How would you scale this system with replication and sharding? What consistency model would you expose to clients?
  • What metrics and observability would you build into this system, and how would you handle backpressure from clients?

“Started with a hash map, which felt obvious, but they pushed on collision handling and memory layout pretty fast.”

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