Best ML Interview Prep Platforms in 2026

The best ML interview prep platform depends on the round you are preparing for. For ML coding rounds, TensorTonic is the strongest choice because you implement models from scratch with tested code, follow a structured plan and rehearse with timed mock assessments, and TensorTonic Plus adds ML system design practice on the same account.

Why did I build an ML interview prep platform?

I built TensorTonic because the interview rounds that mattered most in my own path into ML asked me to implement models and metrics from scratch and explain the trade-offs, and there was no single place to practise exactly that with code that gets checked. I came to machine learning from a biotechnology degree and went on to research and engineering internships at an industry ML team, a large tech company's R&D centre and a corporate research lab, so I prepared for several different ML interview loops.

TensorTonic puts that preparation in one place: you write NumPy or PyTorch implementations in the browser, tests tell you immediately whether they are correct, study plans give you an order to work in, and mock assessments let you rehearse under real time limits. This guide explains which interview round each kind of platform prepares you for, so you can see where TensorTonic covers what you need on a single account.

Which ML interview skills do you need to prepare?

Machine learning interviews test three different skills, which are ML coding, ML system design and applied ML reasoning, and most platforms train only one or two of them. Decide which rounds your target role includes, then choose platforms by round rather than by overall reputation.

• ML coding tests implementation, where you write a model, a metric or a training step from scratch, usually in NumPy or PyTorch.
• ML system design tests architecture, where you design a production ML system end to end, from data and features to evaluation and serving.
• Applied ML cases test judgement on ambiguous problems, where you choose a modelling approach, defend an evaluation metric and discuss trade-offs.

Practising one of these does not train the others, because implementing logistic regression will not teach you to design a recommendation system, and reading system design case studies will not make you faster at writing correct code under time pressure.

What are the best ML interview prep platforms?

The six platforms below cover the three interview surfaces between them, and the table shows which one fits each kind of preparation.

Which platform is best for ML coding interviews?

TensorTonic is the best platform for ML coding interviews if you want a defined plan of from-scratch implementations with tests, quizzes and mock assessments. The Cracking ML plan covers the classical algorithms that implementation rounds ask about, and the 500+ free problems let you keep practising beyond it.

Which platform is best for ML system design interviews?

For ML system design, practise on TensorTonic Plus, which has ML system design problems with an interactive design canvas, so you build designs yourself rather than only reading about them. Practise timed designs out loud, and state the metric before you choose a model.

Which platform is best for applied ML interviews?

Applied ML interviews test whether you can choose and defend a model and a metric, and the fastest way to get good at that is to implement the metrics yourself. TensorTonic's evaluation problems, such as ROC AUC, precision-recall and cross-validation, prepare you for the follow-up questions where you have to compute the metric you just recommended.

Which platform covers a full ML engineer interview loop?

TensorTonic Plus covers the most of a full ML engineer loop on one account, combining from-scratch ML coding, study plans, quizzes, timed mock assessments and ML system design problems. Keeping everything on one account also means one progress history across coding, quizzes, mock assessments and design practice.

What does TensorTonic's ML interview plan include?

TensorTonic's Cracking ML plan has 35 problems, of which 13 are easy, 16 medium and 6 hard, all written in NumPy from scratch without scikit-learn or PyTorch. They are split into six sections, and each section ends with a 10-question quiz.

• Linear models, with 5 problems including linear, logistic, ridge and lasso regression.
• Classification, with 5 problems including k-nearest neighbours, Naive Bayes and a linear SVM.
• Ensemble methods and trees, with 8 problems from decision trees to random forests and gradient boosting.
• Unsupervised learning, with 8 problems including k-means, DBSCAN, PCA and Gaussian mixtures.
• Evaluation and model selection, with 5 problems including cross-validation, ROC AUC and precision-recall.
• Feature engineering and preprocessing, with 4 problems on imputation, encoding, scaling and distance metrics.

The full plan and the mock assessments are part of Pro at $100 a year, and Plus at $168 a year adds the ML system design problems. You can compare both on the pricing page, or read what the free plan includes.

Which ML interview problems should you try first?

Start with these three problems, which each take a short session and show how implementation questions are tested, then open the full Cracking ML plan.

• Gaussian Naive Bayes: compute class priors, means and variances, then predict labels from log posteriors.
• Adam optimizer step: write one Adam update in NumPy, including bias correction and both running moments.
• Sinusoidal positional encoding: build the encoding matrix used by Transformers without Python loops.

Related guides

• How to prepare for an ML engineer interview
• LeetCode for machine learning: where to practice ML coding
• Is TensorTonic free? Free, Pro and Plus compared