Round Structure

In this section, we dive into one of the most important—and often most hands-on—parts of the ML interview process: the practical machine learning coding and modeling round. This is where we demonstrate how we think and work like real ML practitioners.

• We’ll usually work in a Jupyter notebook environment, either in a take-home format or during a live 60–90 minute session.
• The task typically involves a real-world problem like text classification or demand forecasting, where we're expected to deliver an end-to-end ML workflow.
• Unlike algorithm rounds, we’re encouraged to use standard ML libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch) to solve the problem efficiently.
• We’ll be evaluated on how we explore data, engineer features, choose models, tune hyperparameters, and communicate our results.
• This round is about thinking like an ML practitioner, showing that we can handle messy, ambiguous problems using practical tools to deliver meaningful solutions.

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