Motmans & Partners ML Engineer Interview Guide

The process was very structured and the recruiter laid out the full loop upfront, which I appreciated because there were no surprises. It started with a phone screening, then moved to a virtual onsite. My first round was a coding interview with two questions, and the style was very much LeetCode-heavy rather than anything ML-specific. One of the questions was a BFS on a binary tree with a twist, so I’d definitely say the coding portion was the main filter early on. After that, the onsite had two more coding rounds, a general system design round, an ML system design round, and a behavioral interview. The ML design discussion was the most interesting part for me because it was framed around designing a harmful advertisements detection system, so it was less about textbook modeling and more about how I’d think through the end-to-end product and system tradeoffs.

Overall, the process felt professional and well organized. The behavioral and design rounds seemed to matter a lot, not just the coding, and I got the sense they were evaluating level as much as raw implementation ability. The coding questions were still important, though, so I wouldn’t go in assuming this is mostly an ML architecture conversation. I ended up receiving an offer, and my main takeaway is that preparation should be balanced: practice standard coding problems, but also be ready to explain ML system design clearly and talk through behavioral examples in a structured way.