amazon | MachineLearningEng Master’s Full-time | Interview Experience
Interview Date: Not specified
Result: Not specified
Difficulty: Not specified
Interview Process
The interview consisted of a phone screen followed by five rounds of interviews. The phone screen focused on machine learning knowledge, specifically probing into the candidate’s experience with recommendation systems.
In the first part, questions included:
- How to handle cold start in recommendation systems?
- What would your model output for a completely new user with no historical behavior? How do you address bias in the model?
The ML coding section involved explaining the logic of dropout, emphasizing the scaling factor during training. A follow-up question asked what would happen if dropout were enabled during testing, which was related to uncertainty estimation.
The five rounds of onsite interviews included behavioral questions aligned with Amazon’s Leadership Principles.
Technical Questions
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Recommendation Systems
- How to handle cold start?
- Model output for new users and bias handling.
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ML Coding
- Explain dropout logic and the difference between training and inference.
- What happens if dropout is used during testing?
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Statistical Analysis
- Analyzing the impact of a new UI on CTR and CVR.
- Methods for stratified sampling when data is limited.
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Array Manipulation
- Product of Array Except Self (without division, O(1) space).
- Handling zeros in the array and overflow concerns.
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Project Deep Dive
- Learning rate scheduler used during model training.
- Gradient clipping principles.
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Feature Engineering
- Predicting relevance of Amazon search results.
- Discussion on bias-variance trade-off and double descent phenomenon.
Tips & Insights
- Prepare small stories related to Amazon’s Leadership Principles.
- Review relevant papers on bias-variance trade-off and double descent.
- Be ready for deep dives into your project experience, particularly technical details.