Snap Inc. ML Engineer Interview Guide

I started with an HR phone screen that was pretty standard: they walked through my resume, asked me to talk about my background and the projects I’d worked on, and spent some time explaining the interview process and how team matching would work at the end. The main behavioral prompt was to describe a project I was most proud of, and I also had to summarize a past project where I took initiative. That first call felt more like a fit check than anything deeply technical, but it was important to be clear and concise about what I actually owned versus what the team did collectively. In my case, the recruiter said they’d arrange the next interviews, but then I never heard back after that screen.

The process I was expecting, based on what they described, was a fairly full loop: a 1-hour technical LeetCode round followed by a 4-hour virtual onsite with two more LeetCode rounds, an ML system design round, and an ML fundamentals/theory round. Each interviewer also asked a couple of behavioral questions, especially around project ownership and team building. The technical portion sounded like a mix of coding and ML depth rather than just one or the other, so I would have prepared for both algorithmic problem solving and being able to explain ML tradeoffs clearly. The recruiters were described as responsive and the interviewers as very nice, so the vibe seemed positive even though the process was pretty structured. My main takeaway is to be ready to talk through one project in detail, especially where you took initiative, and to expect both coding and ML-specific discussion if you make it past the screen.