OpenAI Interview Questions
Practice 232 real OpenAI interview questions for 2026. Covers top technical tracks — Coding & Algorithms, System Design, ML System Design, Machine Learning, and Data Manipulation (SQL/Python) — across Software Engineer, Machine Learning Engineer, Data Scientist, and Product Manager roles. OpenAI interview questions here focus on clean, production-ready code, rigorous system tradeoffs, and ML-system thinking; this collection is intended for interview preparation with detailed solutions drawn from actual interviews. Expect a coding-heavy loop that rewards clear complexity reasoning and robust testing, plus system-design rounds that dig into multi-tenant architecture, GPU-efficient services, and datastore durability. For Software Engineers, recurring themes include file-backed storage and persistence, multi-tenant IDEs and hosted notebooks, graph/snapshot algorithms, distributed simulation, and GPU/resource accounting. Machine Learning Engineers face model-debugging and transformer KV-cache problems, extensible simulation engines, labeling pipelines and duplicate-detection systems, and real-time collaborative editing buffers. Data Scientists see SQL and experimentation work around churn and free-trial impact plus algorithmic implementations in NumPy. Product Managers should be ready to productize models and define monetization tiers. Use focused coding drills, system-design mocks that involve GPUs and multi-tenancy, ML-debugging exercises, and experiment-SQL practice for best results.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Design a Distributed Rate Limiter
Design a distributed rate limiting system for a large API platform. The platform runs many API gateways and backend services across multiple regions. ...
Implement 1NN with NumPy
Implement a 1-nearest-neighbor (1NN) classifier from scratch using NumPy, then show that the same decision can be expressed as a neural-network-style ...
Design Video Generation Orchestration
Design a scalable system to orchestrate AI video generation (think Sora-style text-to-video). Users submit text prompts to generate videos. For each s...
Design an Instagram-like Feed System
Design an Instagram-like photo-sharing application, focusing on feed generation and delivery. Your design should support: - Users creating posts conta...
Design a Distributed Crossword Solver
Design a Distributed Crossword Solver Design a distributed system that solves crossword-like word puzzles at scale. You are given a rectangular grid o...
Design Online Chess Matchmaking
Design the backend architecture for an online chess platform's matchmaking system. Players submit requests to play ranked or casual chess games. A req...
Design CI/CD Build Caching
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Implement a Simple Memory Allocator
Design and implement a simplified memory allocator exposing malloc(size) and free(ptr) over a single fixed-size heap (a contiguous byte array). The al...
Design a Text-to-Video Generation System
Design a Sora-like text-to-video generation platform. Users submit a text prompt, optional generation settings (duration, resolution, fps, seed, model...
Compute entropy and implement 1-NN
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Design a Slack-Like Messaging System
Design a Slack-like team messaging system focused on sending and receiving messages in real time. Your design should support workspaces with channels ...
Debug MiniGPT and Backpropagate Matmul
This is a hands-on PyTorch screen with two independent tasks. You share a code editor with the interviewer and are expected to run the code, read trac...
Filter Bad Human Annotations
You are given a large training dataset labeled by human annotators. Some of those annotations are low quality — inconsistent, rushed, the result of mi...
Design a sandboxed cloud IDE
System Design: Sandboxed Cloud IDE (Colab-like) Design a multi-tenant, browser-based cloud IDE/notebook that lets users run code inside an isolated sa...
Implement Backprop for a Tiny Network
Implement and explain the forward and backward pass of a small two-layer neural network for classification — first from scratch with NumPy, then with ...
Build a Compose Rating Card
Build a rating card in Android using Kotlin and Jetpack Compose. The card lets a user leave feedback consisting of a star rating and a written comment...
Compute Matrix Prefix Products And Gradients
You are given $N$ square matrices $A[0], A[1], \dots, A[N-1]$, each of shape $D \times D$. Define the inclusive prefix (cumulative) products: $$Y[i] =...
Design a Text-to-Video Generation Service
Design a large-scale text-to-video generation service similar to a modern generative video product (e.g. a Sora-style system). A user submits a text p...
Design a Distributed Crossword Solver
Design a scalable, distributed service that solves crossword-style fill-in puzzles. A request contains a rectangular grid with blocked cells, empty ce...
Improve Training With Noisy Annotators
You are given a labeled training dataset as a Pandas DataFrame. Each row contains feature columns, an observed label, and an annotator_id identifying ...