The interview process included an initial screening stage followed by a final round. The final round consisted of three 45-minute interviews with short breaks in between. The discussions focused mainly on prior experience, research projects, machine learning fundamentals, model evaluation, experimentation, and applied problem-solving in product-oriented settings. There were also behavioral questions and discussion of how I approach failure analysis, data quality issues, and the practical impact of technical solutions.

The interview process started with a short 30-minute introductory interview, where we mainly discussed my previous experience, background, and motivation for the role. After that, I had three interviews in one day. One interview focused more on my previous projects and experience, while the other two were more technical and focused on machine learning theory, practical ML problem-solving, and model selection. The interviewers were very nice, professional, and the atmosphere was comfortable throughout the process.