The interview lasted about an hour and began with introductions, where I shared a brief background about myself — my academic journey, previous roles, and how I secured those positions. I also discussed my recent ICLR paper, explaining the motivation behind it, the core methodology, and the impact of the research. The technical portion focused on machine learning fundamentals, particularly around decision trees and ensemble methods (e.g., Random Forests, Gradient Boosting). I was asked both conceptual and problem-solving questions to evaluate my understanding of how these models work, their advantages, limitations, and use cases. The final segment revolved around a research paper they had shared with me in advance. I summarized my interpretation of the paper, discussed its approach, and answered a few analytical questions to test my depth of understanding and ability to connect it to real-world ML applications.