Amazon·Machine Learning Engineer·Technical Phone Screen
Apr 2026
Breadth-style ML interview for a senior role at Amazon, covering basically the whole ML curriculum in one sitting. Nine questions, fast pace, felt like a written exam more than a conversation. Some were straightforward, a couple had follow-up angles I didn't fully anticipate.
- What is a p-value and how should it be interpreted? What are the common ways people misuse it?
- What are overfitting and underfitting? How do you know which one you're dealing with, and what do you do about it?
- What is causal inference and what methods exist for establishing causality from data?
- What do encoding and decoding mean in the context of machine learning? Give some concrete examples.
- Can you explain gradient descent and backpropagation at a high level?
- What are vanishing and exploding gradients, why do they happen, and how do you address them?
- How do you approach training a model when the class distribution is heavily skewed?
- Your model shows 99% accuracy but is clearly not working well in practice. What's going on and how do you evaluate it properly?
- What is an A/B test? If the results look weird or suspicious, what could explain that and how would you dig into it?
“I gave the textbook definition (probability of seeing data at least this extreme under the null) but fumbled a bit when pushed on what it does NOT mean.” 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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