Machine Learning Engineer · Full Journey · Multiple Types

Amazon — Machine Learning Engineer ❌ Failed

Level: Staff-Level

Round: Full Journey · Type: Multiple Types · Difficulty: 7/10 · Duration: 360 min · Interviewer: Unfriendly

Topics: Machine Learning, Recommendation Systems, A/B Testing, Statistics, Algorithms, Feature Engineering, Bias-Variance Tradeoff, Gradient Clipping, Learning Rate Schedulers

Location: Seattle, WA, US

Interview date: 2025-12-29

Got offer: False

Summary

Round 1: Phone Screen

Question: ML knowledge related to my recommendation system project (cold start problem, handling bias). Also coding on dropout logic.

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Round 2: Onsite

Question: Statistical analysis of a new UI launch and its impact on CTR and CVR.

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Round 3: Onsite

Question: LeetCode problem: Product of Array Except Self. Also, finding the top k largest numbers.

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Round 4: Onsite

Question: Deep dive into my project, focusing on model training details (learning rate scheduler, gradient clipping).

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Round 5: Onsite

Question: Designing relevance prediction for Amazon search results, discussing feature engineering and bias-variance tradeoff.

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Round 6: Hiring Manager

Question: High-level discussion about the team's direction and troubleshooting model performance degradation after deployment.

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Details

Preparation Tips & Key Takeaways

What I Learned

• I need to thoroughly understand the nuances of A/B testing and statistical analysis for UI changes.
• It's crucial to be prepared to discuss the practical aspects of machine learning models, including training, deployment, and troubleshooting.

Recommended Preparation

Machine Learning

• Review common recommendation system techniques and cold start strategies.
• Understand the bias-variance tradeoff and double descent phenomenon.
• Brush up on feature engineering techniques for search relevance prediction.

Coding Practice

• Practice array manipulation problems.
• Be prepared to discuss edge cases and optimizations for algorithmic solutions.

Statistics

• Study stratified sampling techniques and their limitations.

Resources I Recommend

• Research papers on double descent.
• Review statistical analysis methods for A/B testing.

Common Pitfalls to Avoid

• Forgetting scaling factors in dropout implementation.
• Not being able to explain the rationale behind model training choices.

LeetCode similar: LeetCode 238, LeetCode Product of Array Except Self, LeetCode Top K Frequent Elements