Machine Learning Engineer · Onsite · Multiple Types

Amazon — Machine Learning Engineer ✅ Passed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 7/10 · Duration: 240 min · Interviewer: Friendly

Topics: Behavioral Questions, Coding, Machine Learning, System Design, Data Structures, Distributed Systems

Location: Seattle, WA, US

Interview date: 2025-12-01

Summary

Round 1: Project Deep Dive + Leadership Principles (LP)

Question: The Hiring Manager (HM) asked very detailed questions about my projects, requiring a deep understanding.

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Round 2: Coding

Question: Implement LFU cache, design the data structure, implement it, and extend it to a distributed system because the input is a data stream. No need to run the code.

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Round 3: Machine Learning Coding

Question: Implement a GPT decoder-only model with four classes: MultiheadAttention, Feedforward, DecoderLayer, and GPT.

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Round 4: Machine Learning System Design

Question: Design a brand logo infringement system using a search-based approach to find images, and then expand the system.

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Details

Recommended Preparation

Coding Practice

• Focus on implementing data structures like LFU cache.
• Practice extending solutions to distributed systems.

Machine Learning

• Understand the implementation details of GPT models.
• Study different components like MultiheadAttention, Feedforward, and DecoderLayer.

System Design

• Focus on system design for image search-based applications.
• Consider scalability and potential issues.

Behavioral Questions

• Prepare detailed answers to project-related questions.
• Understand the Leadership Principles and be ready to provide examples.

Resources I Recommend

• Review relevant research papers on GPT architecture.
• Study system design principles for large-scale image retrieval systems.

Common Pitfalls to Avoid

• Lack of in-depth understanding of own projects.
• Inability to adapt solutions for distributed environments.