LeetCode covers coding. Not ML.
Grinding LeetCode won't help you design a recommendation system or explain why you'd choose a two-tower model over matrix factorisation.
ModelCandidate
Prepare for ML interviews the way interviewers think
ModelCandidate covers the ML depth that LeetCode ignores: system design, recommendation systems, NLP, ranking, and more. Submit answers, get AI feedback, and actually improve.
Built by a practising Staff MLE who has conducted 100+ ML interviews at top tech companies.
Generic prep tools don't prepare you for ML interviews.
No feedback loop.
Reading answers from a textbook gives you zero signal on whether your thinking is actually interview-ready.
Resources are fragmented.
The best ML interview knowledge is scattered across papers, blog posts, and private prep docs. ModelCandidate puts it in one place.
How ModelCandidate works
Choose a topic
Pick from ML System Design, Recommendation Systems, NLP, Computer Vision, or Ranking. Each topic is structured the way interviewers actually test it.
Write your answer
Answer long-form questions as you would in a real interview. No multiple choice. Real thinking required.
Get AI feedback
Submit your answer and receive a structured score, specific strengths, and concrete improvements, graded against a rubric written by a Staff MLE.
What you'll prepare for
ML System Design
End-to-end ML system design questions covering architecture, trade-offs, and production considerations
5 questions
Recommendation Systems
Prepare for recommendation system design, candidate generation, ranking, retrieval, and evaluation questions.
0 questions
Natural Language Processing
Study NLP model design, evaluation, retrieval-augmented generation, embeddings, and production tradeoffs.
0 questions
Computer Vision
Practice computer vision system design, data strategy, model evaluation, and deployment considerations.
0 questions
Ranking
Prepare for ranking systems, learning-to-rank, metrics, experimentation, and relevance tradeoffs.
0 questions
See it in action
Design a real-time recommendation system for a video streaming platform with 50 million daily active users. Walk through your approach from candidate generation to ranking.
AI feedback preview
Strong structure around candidate generation and ranking, with room to improve online evaluation and cold-start handling.
Sign up free to submit your answer and see AI feedback
Get Started FreeSimple pricing
Free
£0/month
- Flashcards across all topics
- 5 free questions per topic
- No AI grading
Pro
£29/month
- Full question bank
- Unlimited AI grading
- Progress tracking
Start preparing like a Staff engineer.
Free to start. No credit card required.