OpenAI Machine Learning Interview Questions
These are the 10 OpenAI machine learning interview questions AceOffer has reconstructed from candidate reports, sorted by how often each was reported. ML Search / RAG System Design leads at 16×. Every question shows when it was last reported; the catalog refreshes monthly.
Part of the full catalog of OpenAI interview questions, covering every round.
Key facts
- •10 distinct machine learning questions indexed
- •57 candidate reports across those questions
- •Most reported: ML Search / RAG System Design — 16× (last seen March 2026)
- •Refreshed monthly · last updated October 2026
Which OpenAI rounds ask machine learning questions?
The OpenAI rounds below are where these questions come up. Counts are distinct questions, not times asked.
Most reported: ML Search / RAG System Design — 16×
Which OpenAI machine learning questions get asked most?
Sorted by candidate-report frequency. Click any question for the full breakdown.
| Question | Round | Reported | Last seen |
|---|---|---|---|
| ML Search / RAG System Design Design a Retrieval-Augmented Generation (RAG) system, typically framed as an enterprise-grade system that allows employees to ask questions over internal documents (similar… | Tech Deep Dive | 16× | March 2026 |
| Backpropagation An ML coding interview (typically 60–75 minutes) at OpenAI Research covering basic linear algebra and backpropagation. The 60-minute version uses Excalidraw (or… | Tech Deep Dive | 13× | September 2026 |
| Applied Statistics and Probability A 75-minute OpenAI Research onsite round that integrates probability theory, applied statistics, mathematical derivations, and Python/NumPy coding. The round is open-ended and… | Tech Deep Dive | 9× | March 2026 |
| Improve Tool / Image Recognition Model Training OpenAI has a product feature where users upload photos of real-world tools (e.g., hardware, equipment) and ask the model to identify them.… | Tech Deep Dive | 6× | December 2025 |
| LLM Decoding Stopping Time / Probability Distributions You are given an oracle LLM whose inference/problem-solving time T follows some probability distribution with known expected value E[T] = 1. The… | Tech Deep Dive | 5× | July 2026 |
| Multiprocessing / Distributed Neural Network Debugging Upcoming round. Candidate asking if this involves: concurrency bugs (race conditions, deadlocks, duplicate consumption) vs. distributed issues… | Tech Deep Dive | 3× | April 2026 |
| GPU Infrastructure and Kubernetes | Tech Deep Dive | 2× | August 2026 |
| Building a Terraform Unknown infrastructure/provisioning question (candidate had never encountered before). Interviewer provided no feedback for 30 minutes, then indicated… | Tech Deep Dive | 1× | August 2026 |
| ML Fundamentals (Oral) A one-hour oral Q&A session (no coding) covering machine learning theory, ranging from foundational concepts (overfitting, underfitting) through to more advanced topics… | Tech Deep Dive | 1× | November 2025 |
| FP8/BF16 Mixed Precision Training & Numerical Overflow Explain how mixed precision training works when combining FP8 and BF16 formats. Specifically, discuss: (1) why these formats are used together in… | Tech Deep Dive | 1× | April 2026 |
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