Crack your
next interview.
Stop grinding generic LeetCode. Practice the real questions OpenAI, Anthropic and DeepMind actually ask — coding, system design, DL/LLM and classical ML — with instant AI review on every solution.
Real, reported questions from 112+ top labs & startups
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 questionsRReflection AI1 questionPerplexity2 questionsSScale AI18 questionsHugging Face2 questionsDatabricks34 questionsGoogle58 questionsMeta60 questionsAmazon58 questionsApple85 questionsMicrosoft36 questionsNvidia28 questionsNetflix40 questionsTTesla3 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.
Battle Monsters
PreviewClassifier Analysis
PreviewNoisy Human Data Classifier Analysis
PreviewLinear Algebra (BP)
PreviewToy Language Type System
PreviewPython Dependency Version Compatibility Check & Adaptive Binary Search
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.
- ✕Inverting binary trees you'll never touch in an ML role
- ✕Zero coverage of attention, RAG, training or serving
- ✕No signal on what a specific lab's loop looks like
- ✕Generic system design — never ML-specific
- ✓Real ML questions: implement attention, design a recsys
- ✓Coding, system design, DL/LLM and classical ML in one place
- ✓Organized by company so you prep the right loop
- ✓Every question maps to a real, reported interview
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 →