Amazon·Machine Learning Engineer·Technical Phone Screen
Jun 2026
Phone screen for an ML engineer role at Amazon, pure conceptual stuff, no coding. Five topics back to back and the interviewer kept pushing on the 'why' behind every answer, which is where I started to sweat a little.
- What are the main assumptions of linear regression, and why is squared loss the standard choice?
- What is logistic regression, and why does the logarithm show up in its formulation?
- What is a random forest, and how does the feature selection work at each split during tree construction?
- Explain the Adam optimizer and compare it to vanilla SGD. What are the real trade-offs?
- Between a narrow two-layer network and a wide two-layer network, which is more likely to get stuck in a poor local minimum, and why?
“I listed the assumptions fine but fumbled the classification part.” The rest of the author's notes on Machine Learning Engineer interview at Amazon, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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