went through the chime MLE loop this spring, L4/L5 level. sharing because i found almost nothing useful when i prepped.
the process was: recruiter call, then a 45-min technical screen, then a 4-round virtual onsite. total clock was about 5 weeks, which felt long for a fintech company but wasn't the worst i've seen.
phone screen: one coding problem (medium, array manipulation) and about 15 minutes of ML fundamentals. the interviewer asked me to explain precision vs recall in the context of fraud detection, which made sense given their core product. nothing trick-y, but you have to actually know what you're talking about.
onsite: round 1: ML system design. they gave me a prompt around building a transaction risk scoring system. this is where chime is actually interesting to interview with because the domain is real. they care about latency (real-time scoring at checkout), data skew (fraud is rare), and model explainability for regulatory reasons. i spent a lot of time talking about feature engineering from transaction sequences, using something like gradient boosting vs a small neural net for interpretability, and how you'd version and A/B test the model without leaking labels. they seemed genuinely engaged. round 2: coding. two problems, both medium difficulty on leetcode scale. one was sliding window, one was graph-adjacent (BFS variant). 45 min, pretty standard. round 3: ML depth. they picked something from my resume (in my case, a recommender system i'd built) and drilled down for 40 minutes. be prepared for real follow-up questions, not just surface stuff. round 4: behavioral. roughly 5 questions. the usual STAR format. they asked about a time i disagreed with a stakeholder on a modeling approach, and about handling ambiguous problem definitions. standard PM-adjacent questions.
verdict: the debrief took about 2 weeks. i got an offer. the loop was reasonably well-run. the ML system design round is the hardest part and is where i think most candidates get tripped up if they haven't thought about fintech-specific constraints (latency, compliance, fraud label lag).
feel free to ask questions.