Amazon·Machine Learning Engineer·Technical Phone Screen
Jul 2026
Technical screen for an MLE role at Amazon, heavy on ML fundamentals with a fraud-detection framing running through basically everything. The questions were interconnected in a way that made it feel like one long diagnostic rather than six separate topics.
- You have a fraud detection dataset where only about 1% of examples are positive. How do you detect and characterize the class imbalance, and why is accuracy a bad metric here?
- What techniques would you use to handle class imbalance, and how do you decide between data-level approaches like SMOTE versus algorithm-level ones like class weights or focal loss?
- How would you design a validation pipeline for this fraud setting that avoids data leakage and actually reflects the real class distribution?
- Walk me through the bias-variance tradeoff. How do you diagnose which problem you have, and what do you actually do about it?
- For an extremely imbalanced classification problem, which evaluation metrics would you use and when? Compare things like ROC-AUC, PR-AUC, F-beta, precision at k, and calibration metrics.
- Compare Transformers and CNNs in terms of their inductive biases, computational complexity, and when you'd choose one over the other, including for tabular fraud features.
“I jumped straight to 'just use AUC' and the interviewer pushed back asking how I'd even know there's a problem in the first place.”