1.Initial Screening (Phone or Online Assessment) 2.Technical Interviews Often 2-3 rounds, focusing on different aspects of machine learning and problem-solving. Core Areas Explored: Mathematics for ML: Linear algebra, calculus, probability, and optimization. Example: Derive the gradient of the cross-entropy loss function. Algorithms & Data Structures: Example: Implement K-means clustering or explain the complexity of decision trees. Practical Machine Learning: End-to-end ML pipeline design (data preparation, feature engineering, model selection, evaluation). Example: "How would you design a recommendation system for an e-commerce platform?" Programming and System Design: Coding ML algorithms, efficient data handling, and scaling ML systems. Example: "Write a function to compute PCA for high-dimensional data." Domain-Specific Questions: NLP, computer vision, or reinforcement learning, depending on the role. 3.Onsite Interviews 4.Take-Home Assignment 5.Final Round or Hiring Manager Interview 6.Decision and Offer