Crack your
next interview.
Stop grinding generic LeetCode. Practice the real interview questions OpenAI, Anthropic and DeepMind actually ask — coding, system design, DL/LLM and classical ML, all in one place.
Real, reported questions from 26+ top labs & startups
1import numpy as np 2# scaled dot-product attention 3def attention(Q, K, V): 4 d_k = Q.shape[-1] 5 scores = Q @ K.T / np.sqrt(d_k) 6 scores -= scores.max(-1, keepdims=True) 7 w = np.exp(scores) 8 w /= w.sum(-1, keepdims=True) 9 return w @ V
Straight 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.
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, 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.
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.
ExploreBy Company
Questions by lab
Real reported interview questions, scoped to each lab. Pick a company to see its track.
- 01Battle Monsters
- 02Classifier Analysis
- 03Noisy Human Data Classifier Analysis
The 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
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 →