interviewed for an MLE role at citadel quantitative strategies earlier this year. this was for a senior-level position, so take that lens into account. sharing what i actually remember, not a sanitized version.
the recruiter screen: short, maybe 20 minutes. they asked about my ML background, which domains i'd worked in, and whether i was comfortable with both research and production. mentioned that the role was heavily production-focused, not pure research.
technical screen 1: ML systems + coding this was the most interesting round. they gave me a problem where i had to design and implement a simple feature pipeline for a time-series prediction problem. not a full training loop, just the feature engineering piece. i wrote it in python. they asked why i made certain choices around windowing, normalization, and handling missing data. then they asked me to walk through how i'd take this to production: serving latency, retraining cadence, monitoring for distribution shift.
if you haven't thought carefully about monitoring and drift detection, this is where you'll feel the gap.
technical screen 2: applied ML concepts no coding. more like a conversation. they asked about specific model families: why you'd pick gradient boosting over a neural net for a given problem, how to handle class imbalance, what you'd do when your model degrades after 6 weeks in prod. one question i didn't expect: how do you explain a model decision to a trader who doesn't care about ML?
onsite: four rounds. one was a full system design for an ML platform feature (online learning, near real-time inference). one was a coding round closer to a data structures / algo problem. one was a deep dive on a project i'd shipped. and one was behavioral.
the coding round felt a bit out of place honestly. it was more leetcode-adjacent than the rest of the loop, which was very applied. if you're coming from pure research, prep your CS fundamentals more than you might expect.
comp for the level i was at: base around $265k, bonus component that's significant but variable. total package landed well above most big tech ML roles but the bonus is genuinely not guaranteed.
citadel MLE interviews are hard but they test real things. not much leetcode theater, mostly whether you can reason about production ML under pressure.