OpenAI Interview Questions

OpenAI Interview Questions

Practice 274 real OpenAI interview questions for 2026 — real interview questions drawn from actual interviews with detailed solutions. OpenAI interview questions on this page span Coding & Algorithms, System Design, ML System Design, Machine Learning, and Data Manipulation (SQL/Python) and cover roles like Software Engineer, Machine Learning Engineer, Data Scientist, Android Engineer, and Frontend Engineer. For interview preparation expect a focus on production ML systems, scalable services, numerical correctness, experimental design, and communication: interviewers evaluate architecture decisions, tradeoffs, measurable impact, and the ability to ship robust systems under uncertainty. The loop runs stage-by-stage: a recruiter screen, a technical phone screen (coding or design), a virtual onsite with focused rounds (practical coding, system and ML design, model/debug deep dives, and behavioral/mission-fit), then team match and offer; typical timeline is about 4–8 weeks and difficulty is high. Practical coding leans toward real-world engineering problems rather than pure LeetCode puzzles. Software-engineer themes include consistent-hashing sharding, GPU job scheduling for text-to-video, payment systems with exactly-once semantics, and video orchestration. ML tracks emphasize resumable iterators and checkpoint/restore, streaming-entropy numerical stability, 1-NN implementations and debugging model backprop; data science focuses on churn, free-trial A/B tests, and SQL retention analysis. Use project deep-dives and worked examples when you prep.

274 Questions 1 Company08.01.2026
Showing 20 results
Role
OpenAI logo
OpenAI
Hard
Software Engineer Locked

Implement IP Address Arithmetic

This question evaluates proficiency in IP addressing, parsing and validation, binary/address-space arithmetic, CIDR subnet calculation, and formatting...

Coding & Algorithms
5
3
37 people solved
May 12, 2026
OpenAI logo
OpenAI
Hard
Software Engineer

Design a multi-tenant CI/CD platform

Question Design a multi-tenant CI/CD system that runs pipelines triggered by Git activity, isolates tenants from one another, and scales to thousands ...

System Design
17
0
307 people solved
Sep 6, 2025
OpenAI logo
OpenAI
Hard
Software Engineer

Explain your perspective on AI safety

You are working in a company that builds and deploys advanced AI systems (e.g., large language models, recommendation systems, vision models) that are...

Behavioral & Leadership
67
0
476 people solved
Dec 8, 2025
OpenAI logo
OpenAI
Medium
Machine Learning Engineer

Train and analyze a classifier

You are given a labeled dataset for binary classification. Implement an end-to-end Python solution that trains a classifier and analyzes it to a produ...

Data Manipulation (SQL/Python)
27
0
522 people solved
Jul 31, 2025
OpenAI logo
OpenAI
Easy
Software Engineer

Answer project deep dive and cross-functional questions

Behavioral / leadership round prompts You’re asked to cover some or all of the following: 1. Technical deep dive presentation - Prepare a short sli...

Behavioral & Leadership
34
0
383 people solved
Jan 22, 2026
OpenAI logo
OpenAI
Medium
Android Engineer Locked

Design Android MVVM API Architecture

This question evaluates a candidate's skill in designing Android client-side MVVM architecture, testing competencies in layered decomposition, HTTP re...

System Design
9
0
70 people solved
May 23, 2026
OpenAI logo
OpenAI
Medium
Software Engineer Locked

Spreading Contagion on a Grid

This question tests graph traversal and multi-source BFS on a 2D grid, evaluating a candidate's ability to model simultaneous state propagation across...

Coding & Algorithms
3
0
21 people solved
Jun 8, 2026
OpenAI logo
OpenAI
Hard
Machine Learning Engineer Locked

Search Monster Battle Strategies

This question evaluates algorithmic problem-solving around deterministic simulation and state-space exploration, focusing on handling transient state ...

Coding & Algorithms
26
0
207 people solved
Apr 8, 2026
OpenAI logo
OpenAI
Hard
Machine Learning Engineer

Explain what torch.distributed.barrier does

Question In PyTorch distributed training, what does torch.distributed.barrier() do? Follow-ups - Give an example of when you would use it. - What are ...

Software Engineering Fundamentals
14
0
226 people solved
Dec 15, 2025
OpenAI logo
OpenAI
Hard
Machine Learning Engineer

Design an ML search system with RAG

Design an ML search system with RAG System Design: ML-Powered Enterprise Search with RAG Design an ML-powered enterprise search system using Retrieval...

ML System Design
58
0
390 people solved
Jul 15, 2025
OpenAI logo
OpenAI
Medium
Software Engineer

Design in-memory database API

Design an In-Memory Database: Insert, Query, and Indexing Design and implement a minimal, single-process, in-memory database intended to be embedded i...

System Design
54
0
621 people solved
Jul 29, 2025
OpenAI logo
OpenAI
Medium
Machine Learning Engineer

Maintain Entropy for a Streaming Distribution

A stream emits categorical observations one at a time. After each observation, report the empirical Shannon entropy of all observations seen so far: H...

Statistics & Math
3
0
28 people solved
Aug 23, 2025
OpenAI logo
OpenAI
Medium
Software Engineer

Design a chatbot (system design)

Question Design an AI chatbot system with a front-end focus, under the following hard constraints: 1. User messages and conversation history are store...

ML System Design
17
0
263 people solved
Aug 4, 2025
OpenAI logo
OpenAI
Hard
Machine Learning Engineer

Design a production RAG system

Question Design a production retrieval-augmented generation (RAG) system for enterprise document QA. Walk through the end-to-end architecture and just...

ML System Design
28
0
282 people solved
Aug 11, 2025
OpenAI logo
OpenAI
Hard
Software Engineer

How to answer common recruiter screen questions

Question You are in an initial recruiter / phone screen for a software engineering role at OpenAI. The recruiter asks a mix of logistics, employment-h...

Behavioral & Leadership
29
0
326 people solved
Feb 2, 2026
OpenAI logo
OpenAI
Medium
Frontend Engineer

Build a Streaming Chat Input

Implement a minimal front-end chat interface, similar to a stripped-down AI assistant (think a bare-bones ChatGPT). The user types a prompt, submits i...

Coding & Algorithms
21
0
188 people solved
Apr 25, 2026
OpenAI logo
OpenAI
Hard
Machine Learning Engineer

Design a chatbot fallback for unknown questions

Scenario You run a ChatGPT-like assistant. Users sometimes ask questions the model cannot answer reliably (unknown/uncertain/needs up-to-date facts). ...

ML System Design
10
0
132 people solved
Dec 15, 2025
OpenAI logo
OpenAI
Easy
Software Engineer

Design a Payment System

Design a Payment System Design the backend payment system for an online platform that charges customers for goods and services (for example, a marketp...

System Design
1
0
17 people solved
Mar 16, 2026
OpenAI logo
OpenAI
Medium
Machine Learning Engineer Locked

Design Real-Time Collaborative Editing

This question evaluates competence in distributed systems, real-time synchronization, concurrency control, data modeling, and automated conflict-resol...

System Design
11
0
173 people solved
Mar 11, 2026
OpenAI logo
OpenAI
Hard
Data ScientistSenior+

Design and analyze a free-trial A/B test

A/B Test Design: 1‑Month Free Trial Impact on Paid Subscription Conversion You are evaluating whether offering a 1‑month free trial increases paid sub...

Analytics & Experimentation
30
0
227 people solved
Oct 13, 2025

Frequently Asked Questions

How difficult are OpenAI interview questions for software and ML roles in 2026?
OpenAI interviews in 2026 are challenging but not purely puzzle-driven: they reward practical engineering, systems thinking, and domain depth. Expect coding rounds that favor real-world tasks (reading and modifying components, debugging, performance-minded implementations) over contrived LeetCode riddles, plus heavy system and ML design for senior roles. Difficulty varies by track: Software Engineer interviews emphasize scalable distributed systems and orchestration; Machine Learning and Research tracks dig deeper into math, experiment design, and paper-level reasoning. Overall the loop is selective; prepare for multi-hour virtual onsites and high bar on correctness, clarity, and tradeoff justification.
What is the OpenAI interview process stage by stage and where do different question types appear?
The process typically starts with a recruiter or sourcer screen (30 minutes) covering fit and logistics, then a technical phone screen or hiring manager call that mixes coding and role-specific questions. Many loops include a paid or take-home exercise in applied roles, then a virtual onsite of four to six rounds: coding/debugging, system or ML systems design, a technical deep dive on your past work, and behavioral/mission-alignment conversations, followed by team-match interviews. Coding and Algorithms and Data Manipulation show up in early screens and onsite coding rounds; System Design and ML System Design dominate senior SWE and MLE rounds; Data Scientist rounds focus more on SQL, experiments, and A/B analysis.
How should I schedule my preparation and what timeline is realistic before interviewing at OpenAI?
Plan a 4–8 week preparation block aligned to the role. Weeks 1–2: solidify fundamentals—data structures, Python or chosen language fluency, and SQL skills with joins, windows, and CTEs. Weeks 3–4: practice role-specific system problems and ML fundamentals—distributed systems, caching, consistency, and experiment design. Weeks 5–6: run mock onsites, do timed coding, and prepare technical deep dives (projects, papers, metrics). Final 1–2 weeks: polish behavioral stories, team-fit pitch, and revisit tricky system/ML topics like checkpointing, numerical stability, and orchestration. Allow extra time if you must rebuild ML coding from scratch or reproduce papers.
What specific subtopics and problem types recur for OpenAI roles (coding, system design, ML, and data)?
For Software Engineers expect distributed-systems themes such as consistent hashing rings with virtual nodes, GPU job schedulers for text-to-video workloads, exactly-once payment processing, versioned social follow graphs, and video generation orchestration. Machine Learning Engineers see resumable iterators with checkpoint/restore, streaming entropy and numerical stability, 1-NN implementations and matrix gradient tricks, debugging MiniGPT-style training, mining novel images, and noisy-annotator strategies. Data Scientists focus on SQL for repeat churn, free-month promotion A/B tests, signup/retention lift calculations, and experiment measurement. Mobile and frontend roles weight architecture (MVVM, Compose) and model-usage quotas.
What are standout preparation tips and common pitfalls to avoid for OpenAI interviews?
Emphasize production-ready thinking: write clear, testable code, explain failure modes, and design for observability and reproducibility. For ML/research tracks, be ready to discuss papers, experimental design, statistical power, and how you'd debug and reproduce training runs. Avoid over-optimizing for abstract puzzle tactics; interviewers want debugging instincts, tradeoff reasoning, and a path to shipable systems. Common pitfalls include ignoring edge cases (concurrency, exactly-once semantics), skimming data-quality implications in experiments, failing to justify metric choices, and not framing past work in terms of tradeoffs and measurable impact during technical deep dives and team-match conversations.

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