cleared the plaid MLE loop in Q1 2026. l5-equivalent, applied to the fraud/risk ML team. writing this up because plaid's ML interview isn't widely documented and it's a bit different from the usual big-tech MLE format.
first thing to know: plaid's ML problems are heavily applied/domain-specific. they care about ML for fraud detection, identity verification, transaction categorization, income estimation. if you've only done NLP or computer vision, you're not out of the running but you should spend time understanding the tabular/structured data + fintech fraud space.
the loop: recruiter screen ML phone screen (60 min): one coding problem (medium, Python), plus a 20-minute ML conceptual discussion. they asked about class imbalance handling, which is very on-brand for fraud/anomaly. virtual onsite, 5 rounds: ML system design (build a transaction categorization system end to end) ML theory (probability, stats, model calibration, feature engineering for financial data) coding (one algorithm problem, one more applied coding problem - they gave a dataset and asked me to write feature extraction logic) cross-functional: how do you work with product and eng stakeholders when your model creates latency tradeoffs behavioral
what they probe in the ML system design:
for transaction categorization specifically, they asked about: feature choices (merchant name parsing, MCC codes, amount distributions, user history), model selection tradeoffs (boosted trees vs neural, latency constraints), how you handle new merchants with no history (cold start), and monitoring drift over time. this is real stuff that plaid actually ships, not contrived.
the ML theory round:
not trivia. they asked me to derive precision/recall for an imbalanced fraud scenario and explain why AUC can be misleading there. we talked about model calibration for risk scoring. they asked about SHAP for model explanations to external partners. expect depth, not breadth.
one thing that surprised me: the cross-functional round was scored seriously. they wanted examples of influencing without authority and communicating uncertainty to non-technical stakeholders. ML engineers at plaid clearly own significant product surface area.
prep focus: tabular ML (not LLMs), imbalanced classification, model monitoring, Python coding fluency, and have a real point of view on fraud/risk ML.