went through the netflix MLE loop last month. sharing notes because when i was prepping i couldn't find a good first-person account from someone doing the recsys/ML side specifically (as opposed to the general SWE loop).
my background: 6 YOE, mostly recommendation systems, some NLP. applied for a senior MLE role on the personalization team.
the loop structure:
round 1: ML depth. this was the most substantive round. the interviewer wanted to go deep on how netflix might think about ranking content for a heterogeneous user base. we talked through cold-start problems, how you handle sparse interaction data for new users, trade-offs between collaborative filtering vs content-based approaches, and when you'd actually use a two-tower model versus a simpler logistic regression. they were not looking for buzzwords. when i said 'i'd try a transformer-based approach here' they immediately asked why, and what the latency/cost trade-off is.
round 2: ML system design. design a recommendation system for a new content type (think: podcasts, games, something not just video). 45 minutes, whiteboard style but remote so it was a shared doc. i started with feature definition, moved to training pipeline, then to serving latency requirements. the interviewer kept asking 'what changes if this has to return in 100ms?' which is a very netflix question.
round 3: coding. this surprised me. not a pure DSA problem. they had me write code to process a stream of user events and maintain a running recommendation list with some recency weighting. felt like a real engineering problem, not a puzzle. medium difficulty, more about clarity and correctness than raw speed.
round 4: behavioral x2. same as everyone else says. individual contribution, disagreement, ambiguity. they pushed hard on 'what would you have done differently.' it's not a gotcha, they just want to see you're reflective.
what i'd tell someone prepping: know your fundamentals cold: bias/variance, regularization, why your loss function choice matters be ready to talk about A/B testing for ML features specifically. online metrics vs offline metrics is a real conversation the culture piece is not a soft add-on at netflix. the 'keeper test' question is reportedly real (would your manager fight to keep you?). frame your behavioral answers around that
got the offer. comp data: posting separately in the comp thread.