Interview process
Round 1: Virtual interview with two interviewers (only one attended in my case). This was primarily a discussion of my background and experience.
Round 2: Approximately two-hour onsite panel interview. It was scheduled for three interviewers, but four attended. The interview consisted of walking through my background followed by an ML system design exercise on a whiteboard.
Interview experience
The hiring manager drove most of the technical discussion and challenged nearly every technical statement I made. The interview felt like a stress test, so be prepared to justify your design decisions and assumptions in detail.
One aspect I found challenging was that while I was presenting my background, the hiring manager appeared disengaged, which made it difficult to gauge whether my experience was being fully considered.
I was also surprised that the interview focused heavily on traditional machine learning topics (model development, feature engineering, training, and evaluation). Based on the job description, I had expected more emphasis on backend engineering, distributed systems, infrastructure, deployment, and production ML.
Advice for future candidates
Ask about the interview format in advance so you know whether to prepare for ML system design, coding, or model-development questions.
If your experience spans both software engineering and machine learning, actively guide the conversation toward the areas where you provide the most value. The interviewers will naturally ask follow-up questions based on the topics you introduce.
If the role truly requires strong model-development knowledge, spend time reviewing core ML concepts and practicing ML system design problems before the onsite.