went through the cloudflare ML engineer loop earlier this year. not a ton of info out there so posting this in case it helps someone.
context: i was applying for a role on their bot detection / traffic intelligence team. your mileage may vary depending on which ML team you're targeting.
recruiter screen. 30 min. nothing technical, mostly a scope conversation about what the team does and what i'd be working on. they were upfront that the ML work here is heavily applied and production-focused, not research-heavy. if you're looking for a place to publish papers, this probably isn't it.
technical phone screen. one hour, two parts. first half was ML concepts: they asked me to explain gradient boosting in plain terms, then we got into how i'd handle class imbalance in a binary classification problem (their use case: distinguishing bot traffic from human traffic). second half was a coding problem, medium-ish difficulty, involving string parsing and some tree traversal. not what i expected for an ML role but it makes sense -- you're writing production code, not just notebooks.
onsite (4 rounds). ML system design: design a real-time anomaly detection system for network traffic at Cloudflare's scale. they care a lot about latency constraints and the tradeoff between model accuracy and inference speed. feature engineering discussion was substantive. coding: two standard DSA problems. both were medium LC difficulty. one involved intervals, one was a graph problem. nothing crazy but you need to be solid. applied ML: deep dive on a past project. this was the best round honestly. felt like a real engineering conversation. they pushed on 'how would you retrain when distribution shifts' and 'how do you detect model degradation in prod'. behavioral: very thorough. specific questions about handling ambiguous data quality issues, cross-functional work, and a time i had to kill a project. cloudflare culture questions woven in.
verdict. i got an offer at what i'd call senior MLE level. base was in the range i expected for a remote role with Cloudflare. comp structure is base + RSUs + bonus but the equity vesting was 4-year with a 1-year cliff, standard.
if i were coaching someone: nail the ML system design, and know your production ML story cold. the applied ML round carries a lot of weight.