← Back to Directory

OpenAI interview notes, page 60 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
Interviewed at OpenAI for what seemed like a software engineering role. One coding question the whole time, basically build a mini interpreter from scratch. Not the hardest thing I've done but there were definitely a few moments where I second-guessed my approach.
  • Given a list of instructions in a simple toy programming language (like 'SET x 5', 'ADD x 3', 'PRINT x'), write an interpreter that runs those instructions and prints the output of any PRINT commands.

“My first instinct was to reach for something overly clever, like building a class hierarchy for each instruction type.”

View Post

OpenAI·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Short product strategy question from what looked like an OpenAI PM screen. Not much context around the interview itself but the question was meaty enough to chew on.
  • Which industry do you think would benefit the most from enterprise ChatGPT?

“This one looks open-ended but you can spiral fast if you don't anchor early.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a software engineering role at OpenAI and got a grid infection spread problem. Pretty classic BFS variant but the edge case indexing tripped me up more than I expected.
  • Given a 2D grid where cells are either infected ('X') or healthy ('.'), each day the infection spreads from every infected cell to all 8 neighboring cells. How many days until the infection can no longer spread?

“Multi-source BFS from all starting X cells, track the max depth, done.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Got a coding question at OpenAI for a software engineer role that was deceptively simple on the surface but had a lot of edge case landmines buried in it. The kind of problem where you think you're done and then realize you've built something that breaks on its own test cases.
  • Design and implement serialize and deserialize functions for a string-to-string map, without using any built-in serialization libraries, such that the format is fully invertible even when keys and values contain arbitrary characters like colons, commas, or pipes.

“The naive instinct is to just join everything with some delimiter and call it done.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineering role, themed around building an online chess platform. The interviewer seemed checked out the whole time, giving one-word acknowledgments and moving on with zero discussion. Ended early, which said everything.
  • How would you design the matchmaking system for an online chess platform?
  • How would you improve the speed or efficiency of that matchmaking system?
  • How would you handle player disconnections and reconnections mid-game?
  • How would you design the database schema for an online chess application?

“This was the main prompt that everything else branched off from.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Technical phone screen for an ML Engineer role at OpenAI, focused pretty heavily on distributed training internals. One main question with a few follow-ups that spiraled into some territory I wasn't fully prepared for.
  • In PyTorch distributed training, what does torch.distributed.barrier() do, and when would you actually use it?
  • What are the risks of calling barrier() incorrectly, and how does misuse connect to deadlocks and performance degradation?

“I knew the basic answer: it's a synchronization primitive that blocks all processes until every rank in the group hits the call.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a data science role at OpenAI, just the one question about a past project. Short and vague as far as interview experiences go, not much to report.
  • Walk me through a past data science project you've worked on.

“Pretty open-ended.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role. The prompt sounds flashy but it's really just a job scheduler problem, and the interviewer went deep on failure recovery and compute efficiency.
  • Design a large-scale video generation system (essentially a distributed job scheduler with GPU resource management).
  • How would you handle worker failures mid-job: resume from a checkpoint or restart the entire job from scratch?
  • How do you prevent wasted GPU compute when a job fails partway through?

“The name threw me for a second but once I reframed it as a job scheduler the structure clicked.”

View Post

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

Jun 2026
This was a deep system design round at OpenAI for an ML engineer role. The whole thing was basically one giant open-ended prompt about building a sensor intelligence system from scratch, and they expected you to drive the conversation yourself with almost no guardrails.
  • Design a complete real-time sensor intelligence system from ambiguous product requirements, covering everything from use case definition and sensor selection through to model training, inference, and post-launch monitoring in a resource-constrained environment.
  • How would you identify the right use cases and success metrics when the product requirements are intentionally vague?
  • Walk through how you would choose sensors for this system and justify the trade-offs between different options.
  • How would you design the data collection, labeling, and training pipeline for a sensor-based ML system?
  • How do you handle real-time inference under strict latency and hardware constraints in a continuously running system?
  • How would you address privacy concerns when continuously collecting live sensor data from users?
  • How would you evaluate the model before launch and monitor its performance afterward in production?

“This is the kind of question where the scope is so wide that you can easily spend 20 minutes on the wrong layer and never recover.”

View Post

OpenAI·Data Scientist·Technical Phone Screen

Jun 2026
Brutal take-home style question for a Data Scientist role at OpenAI, basically a full debugging and ML systems design exercise wrapped into one prompt. The kind of thing where you either know your PyTorch internals cold or you're guessing. No fluff, no behavioral warmup.
  • Given a broken minimal causal language model in PyTorch where training loss never improves and sometimes goes NaN, identify every bug across the architecture, masking, numerics, and training loop, explain what each bug causes and why, and provide a corrected implementation.
  • Write three PyTest-style unit tests that would catch the bugs in the faulty model before training starts, covering things like tensor shapes, mask correctness, and gradient flow.
  • Derive the time and memory complexity of scaled dot-product attention in terms of batch size B, number of heads H, sequence length S, and head dimension d_k.
  • Propose two changes that reduce memory usage during attention without significantly degrading perplexity, and discuss the trade-offs of each.

“This was the meat of the whole thing.”

View Post