1,230 questions reported asked in the last 12 months
Crack Your Next Interview
Practice the real questions OpenAI, Anthropic, Meta and Google actually ask — coding & LeetCode-style, system design, DL/LLM and classical ML — with instant AI review on every solution.
Real, reported questions from 345+ top labs & startups
3,285 questions · 2,713 coding & LeetCode-style · system design, DL/LLM & classical ML
Filter the whole bank to the last 6 or 12 months, or sort by most recently asked.
Sit a mock interview with an AI interviewer
It runs a real reported question the way an interviewer does — states the problem, lets you think, probes when you hand-wave, and won't give you the answer. At the end it files a debrief: a hire verdict, scores across seven dimensions, and what to practise next.
Browse by company
Real interview questions, by company
Reported questions scoped to each lab & startup. Pick a company to see exactly what they ask.
xAI27 questionsGoogle DeepMind10 questionsSScale AI18 questionsDatabricks34 questionsGoogle91 questionsMeta91 questionsAmazon465 questionsApple85 questionsMicrosoft39 questionsNVIDIA28 questionsNetflix40 questionsTTesla24 questionsIIBM16 questionsUber90 questionsStraight from real loops
Real interview questions, every type
From system design to ML coding to flashcards — drawn from real, reported interviews. Here are a few of the most-viewed.
System Design for ML
Design recsys, serving stacks & data pipelines.
Coding & Leetcode-style
Implement attention, kNN, training loops from scratch.
ML Fundamentals & Algorithms
Bias-variance, gradients, classical ML.
Deep Learning & Architectures
Transformers, CNNs, normalization, optimization.
LLMs & Prompt Engineering
RAG, fine-tuning, evals & inference.
Forward Deployment Engineering
Ship ML with customers: integration, data & real-world debugging.
Flashcards & quick recall
Lock in fundamentals across every topic.
Classifier Analysis
PreviewBattle Monsters
PreviewNoisy Human Data Classifier Analysis
PreviewLinear Algebra (BP)
PreviewToy Language Type System
PreviewTransformers Debugging
PreviewEverything in one place
Everything you need to prep — in one place
Scroll through what you get: real company questions, an in-browser coding pad with AI code review, flashcards, daily paper digests, an ML job board, and progress tracking.
Curated company questions
Hundreds of expert-reviewed ML questions, filterable by lab — Google, Meta, OpenAI, Anthropic and more.
ExploreIn-browser coding
Write and run ML code in the browser with LeanCode and the coding-practice editor — tests run instantly.
ExploreCorrectness: allows i == j — likely the failing case.
Complexity: O(n²) — a hash map gets you to O(n).
AI code review
Get instant AI feedback on the code you write — correctness, complexity, code quality, and a targeted hint — without spoiling the full solution.
ExploreWhat problem does RoPE solve in transformers?
Flashcards
Quick recall decks across core ML, deep learning, and system design to lock in the fundamentals.
ExploreMixture-of-Experts routing at scale
Long-context KV-cache compression
Personalized Paper Digest
AI-driven digests of new arXiv papers, tailored to your research interests — delivered daily.
ExploreML Engineer, Inference
OpenAI · Remote
Research Engineer
Anthropic · Remote
Applied Scientist
Databricks · Remote
ML Job Search
Curated ML / AI roles from top labs and startups, matched to your profile and target companies.
ExploreTrack your progress
Attempt history, best scores, streaks, and per-company performance so you always know what to study next.
ExploreThe honest pitch
Why not just LeetCode?
Generic algorithm grinding doesn't reflect how ML teams actually interview. Here's the difference.
- ✕No idea when — or whether — a question was ever actually asked
- ✕Textbook puzzles with no link to any real interview
- ✕No idea which company asks what, or how often
- ✕Zero coverage of attention, RAG, training or serving
- ✕You get a green checkmark — never feedback on your code
- ✓Dated: each question shows when it was last reported asked, and how often
- ✓Same algorithm and data-structure problems — but reported from real loops
- ✓Organized by company, so you prep the loop you're actually in
- ✓Plus ML system design, DL/LLM and classical ML in the same place
- ✓Instant AI review on correctness, complexity and code quality
AI code review
Solving it isn't enough. You have to show you can write great code.
Anyone can reach an answer. Interviews reward code that's clean, correct, and well-structured — and you only get there with real feedback. Every solution you write gets an instant AI review — correctness, complexity, and code quality — like a senior engineer reading over your shoulder, so your coding actually improves.
Featured guides
Field notes from the loop.
How to scope an ML system design answer
A repeatable structure: requirements, data, model, serving, and the metrics that decide it.
Read guide →Why LeetCode alone won't get you into Meta or OpenAI
The five ML coding patterns that labs actually test — and why algorithm grinding misses all of them.
Read guide →Career switch to AI engineering: system design or algorithms first?
Which to study first depends on your target role. Includes a 90-day roadmap.
Read guide →