← Back to Directory

OpenAI interview notes, page 26 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

May 2026
System design interview at OpenAI for a software engineer role, centered entirely on implementing a memory allocator from scratch. The question was dense and covered a lot of ground, from data structure justification to fragmentation handling to complexity analysis. Felt like a mini systems course crammed into one session.
  • Design and implement a simulated memory allocator with allocate(size) and free(ptr) operations over a fixed-size contiguous byte array. Cover data structure selection, alignment, fragmentation handling, coalescing of adjacent free blocks, and time/space complexity. Also discuss first-fit vs best-fit vs next-fit trade-offs and how you'd test and benchmark it.

“This was a lot.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

May 2026
OpenAI coding interview, looked like a standard data structures round but the graph traversal question had enough moving parts to keep me on my toes for a while. No fluff, just code.
  • You have a social network class where users can follow each other. Given a user U, implement a 'people you may want to follow' feature: find all users that U's followees follow (friend-of-friend), rank them by how many of U's direct followees follow them, exclude U and anyone U already follows, return the top K results. Tie-break lexicographically. Also discuss the time and space complexity in terms of average degree and depth-2 expansion.

“The core traversal wasn't the hard part.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

May 2026
OpenAI software engineer interview with a filesystems-flavored coding problem that had a sneaky follow-up. The core question was straightforward enough but the symlink extension is where things got interesting.
  • Implement the Unix `cd` command: given a current working directory as an absolute path and a target path (which can be absolute or relative, and may include `.`, `..`, multiple slashes, or trailing slashes), return the resulting normalized absolute path.
  • Follow-up: extend the solution to handle symbolic links. Given a map of path-to-target symlink definitions, resolve symlinks transparently during path traversal. Also handle or detect symlink cycles.

“I've seen path normalization come up before but usually as a throwaway utility, not the whole question.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026
Went through a technical ML interview at OpenAI for an MLE role. The whole session was basically one deep-dive question about noisy labels and annotator disagreement, which sounds narrow but spiraled into a lot of interconnected territory fast.
  • How would you build a classifier when your training labels come from multiple annotators who have different skill levels and frequently disagree with each other?
  • How do you estimate how reliable each annotator is, especially when you don't have a gold-standard ground truth to compare against?
  • What does it mean for an annotator to be biased rather than just unreliable, and how would you model that distinction?
  • Could annotator identity itself be used as a feature in the model, and when would that actually help?
  • How do you evaluate a classifier's performance when the ground truth labels are themselves uncertain or contested?

“This started simple enough and I walked through majority vote pretty confidently, but then they kept pushing.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at OpenAI, one big open-ended question about building a distributed task scheduler from scratch. Felt like they wanted to see how far you could go before running out of things to say, and I definitely found that ceiling.
  • Design a distributed task scheduler similar to Quartz or Airflow that can schedule and execute background jobs across a fleet of machines. Cover the API for submitting, canceling, and querying jobs, scheduling semantics like cron, fixed-delay, fixed-rate, one-shot, and DAG dependencies, a persistent task store, leader election to prevent double-execution, worker pool management, failure recovery, exactly-once vs at-least-once execution guarantees, retries with backoff, timeouts, fairness across tenants, and observability.

“This question is basically a whole system in one prompt.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026
Got a pretty gnarly ML theory question at some point during what felt like a technical screen for an ML engineer role at OpenAI. The kind of question where you think you know the answer and then halfway through your explanation you start second-guessing yourself.
  • How would you express 1-nearest neighbor classification as a neural network forward pass, specifically using a linear layer followed by a softmax?

“This one took me a minute to get grounded.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026
Went through a system design round at OpenAI for a software engineer role. The question was a payment platform and it went pretty deep. I got through it but left feeling uncertain about whether I came across as engaged enough.
  • Design a payment platform. Walk through the architecture, key components, and how you'd handle scale and reliability.

“I'd prepped using a YouTube video on this exact topic, which gave me false confidence.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

May 2026
Coding round at OpenAI for a SWE role, one meaty design-and-implement question about a transactional key-value store. Not a ton of hand-holding, just build the thing.
  • Design and implement an in-memory key-value store that supports set, transactional begin, commit, and abort operations.

“The basic set/get part took maybe five minutes.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026
OpenAI ML engineer interview that went deep on classifier architecture. The whole session was basically one long coding and design problem about building a version detection pipeline from scratch, which sounds narrow but ended up covering a lot of ground.
  • Build a version-check pipeline using three separate binary classifiers instead of a single multi-class head. For each classifier, define inputs and outputs, implement the training loop with BCE loss, and implement inference that returns both a probability and a thresholded decision.
  • After building the three classifiers, combine their outputs into a single version label and discuss the trade-offs: why use independent binary classifiers versus one multi-class softmax head, including things like overlapping versions, per-head calibration, and how easy it is to add a new version later.
  • How do you handle conflicts when more than one binary classifier fires at the same time, i.e. multiple heads return a positive decision for the same input?
  • How would you tune the decision threshold for each binary classifier head independently, and what factors would drive those choices?

“This took me a while to get into the right headspace.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026
Technical interview for an ML Engineer role at OpenAI, focused entirely on debugging a broken Transformer implementation in PyTorch. One long coding problem, multiple bugs to find, and a lot of pressure to be systematic about it.
  • You're given a buggy Transformer implementation and a training script. The forward pass and training loss don't match a reference. How do you systematically find and fix all the issues?

“This was the whole interview basically.”

View Post