The hardest part for me was that the process never felt like just one kind of interview. It started with a HackerRank online assessment that had two questions, one easy and one medium, and I had an hour to finish it. After that, I had a phone screen that mixed a LeetCode-style medium coding question with behavioral questions. The coding itself was manageable, but the interview quickly shifted into AI/ML depth, so I had to be ready to talk through my resume and explain the work behind my projects rather than just the results.
From there, I was invited to a loop that felt pretty research-heavy. In my case it was a five-person loop, with one interviewer after another, usually scheduled the same day or split across two days. Everyone I spoke with was a research scientist on the team. The questions were less about broad system design and more about whether I could reason carefully about research problems. I was asked about Gaussian Mixture Models, the sparse method of Transformer attention, and what I would do if I had to report a small correlation. There was also a coding round with tree traversal and dynamic programming; the tree problem needed memoized recursion, and the DP question was a knapsack-style problem. That part was moderate to difficult mainly because of the time pressure.
The behavioral portion was very resume-driven, especially around the most difficult project I had worked on. Overall, the process was fair and the recruiting communication was clear, but it was definitely rigorous and leaned heavily toward research depth plus practical coding ability. I didn’t get an offer, so my main takeaway is to prepare for both ML fundamentals and research discussion, not just coding practice.
Prep tip from this candidate
Be ready to explain Gaussian Mixture Models, sparse Transformer attention, and how you would interpret or report a small correlation. Also practice a tree-traversal memoized recursion problem and a knapsack-style DP under time pressure, since that combination showed up in the loop.
