Core DSA Questions were asked for the majority of the interview. And only towards the later half of the interview, was I asked about my experience in Machine Learning. The questions about my research and ML experience were relatively easy because I was familiar with them but the initial part of DSA made a little nervous while answering these questions as well.
Interview questions [1]
Question 1
What does the vocabulary space of a Language Model mean?
Surprisingly straightforward — I expected a tougher challenge for a machine learning role. After a quick recruiter screen, the first technical round focused on implementing K-means clustering, which felt familiar. Handling edge cases for empty clusters was tricky, though. What really helped me prep were the algorithm explanations on PracHub, which gave me confidence going in. The final interviews were a mix of problem-solving and behavioral questions, and in the end, I received an offer that I accepted. Overall, it was a decent experience.
Interview questions [1]
Question 1
implementing K-means clustering from scratch and handling empty cluster edge cases
three rounds, each has coding + ml basic + resume related questions
understand all the details in resume is important, since might go very deep down the project you have worked on
The first round was mainly a CV walkthrough.
The second round focused in depth on one specific project.
Both rounds also included LeetCode medium-level problems.
The third round was with the hiring manager with both project and problems for their business.