interviewed for an MLE role at Block (the payments/risk team, not the bitcoin side) earlier this year. took the offer, but sharing the process because there was almost nothing specific about this team online.
role context: the team builds fraud and risk models. it's applied ML with a lot of feature engineering and operational complexity, not research. if you're expecting to talk about transformers all day, wrong interview.
phone screen (technical) started with a coding problem: implement a sliding window feature computation (something like rolling 7-day transaction velocity per user). this is a hint at the actual job. then a conceptual ML question: "you have a model with 98% accuracy on historical fraud data. why might this be misleading?" they want: class imbalance, temporal leakage, distribution shift. give all three.
onsite (5 rounds) ML systems design: design a real-time fraud scoring system. the classic, but they push hard. what does the feature store look like? how do you serve at low latency (think sub-100ms on a payment authorization)? what happens when the model needs to be retrained and deployed without downtime? i spent 15 minutes on the serving layer alone. ML fundamentals: gradient boosting deep dive (XGBoost/LightGBM internals), precision/recall tradeoffs in high-stakes settings, calibration. one tricky question: "your model's AUC is high but it's miscalibrated on the high-velocity segment. what do you do?" have a real answer. coding: two problems. one was a feature transformation pipeline in python (clean, efficient). second was a graph problem for network analysis (transaction graph, find suspicious clusters). not easy. experimentation/analytics: A/B testing a new model version in production. how do you handle the fact that fraud is adversarial and your control/treatment contaminate each other? this tripped me up and i gave an incomplete answer. behavioral: cross-functional collaboration, handling model failures in prod, disagreeing with stakeholders about model risk threshold.
comp (i'll share rough numbers): L5 equivalent, SF-based, base around $210k, RSUs valued at roughly $300k vesting over 4 years at the time of offer. nothing groundbreaking for SF MLE but reasonable for the complexity of the work.