going to give you the real version of the amazon MLE loop, not the polished one. i just finished it for an L5 role on the personalization team. six rounds including a phone screen.
phone screen: one coding problem (medium lc, tree traversal) and two conceptual ML questions. the ML questions were: 1) how do you evaluate a ranking model? 2) what's the difference between bias and variance and what would cause each in practice? if you've done any real ML work these aren't hard but they screen out people who memorized terms without understanding them.
onsite round 1: coding. two coding problems. medium difficulty, arrays and graphs. nothing ML-specific. this is where people expecting a pure ML interview get burned. amazon MLE interviews have significant SWE content. you need to pass the coding bar, period.
onsite round 2: ML systems design. design a recommendation system for amazon product search. this is where depth matters. they don't want textbook collaborative filtering. they want: how do you handle cold start? what features do you pull? how do you balance exploration vs exploitation? how do you set up your offline eval and then your online AB test? i talked about candidate generation then ranking as a two-stage approach and they seemed very familiar with that framing (probably because that's how their systems actually work).
onsite round 3: applied ML. walk through a real model you built end to end. same as DS loops. they want the prod story including failure. i talked about a recsys i built that worked great offline and was mediocre online and we figured out it was leaking future information during feature engineering. that kind of war story lands better than a success story.
onsite round 4: ML fundamentals. deep questions on things like: gradient descent variants and when you'd use each, regularization, handling imbalanced classes, when you'd use tree-based vs neural approaches. more theoretical than the applied round.
LP round: deliver results and think big. have actual impact numbers.
honest take on the loop: it's rigorous and fair. i didn't feel like i was fighting arbitrary gotcha questions. the SWE component is real though. if your coding is weak you won't clear the bar even if your ML depth is strong. fix that first.
offer came through, l5, AWS AI services team base around $195k + RSUs, details approximate.