Machine Learning Engineer · Onsite · Multiple Types

Netflix — Machine Learning Engineer ❌ Failed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 7/10 · Duration: 180 min · Interviewer: Very Unfriendly

Topics: Algorithms, System Design, Machine Learning, Recommendation Systems

Location: Los Gatos, CA

Interview date: 2025-02-15

Got offer: False

Summary

Round 1: Coding

Question: K Closest Points - LeetCode problem.

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Round 2: System Design

Question: Design an ML job scheduler. I lacked experience in this area, and the interviewer became increasingly unimpressed.

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Round 3: System Design

Question: Design video recommendations for the homepage. I spent more time discussing the number of services required rather than features, modeling, or evaluation.

Details

Preparation Tips & Key Takeaways

What I Learned

• I need to gain experience designing ML job schedulers.
• It's important to balance service-level discussions with feature engineering and evaluation during system design interviews.

Recommended Preparation

Coding Practice

• Review common LeetCode problems.

System Design

• Study ML system design principles and job scheduler architectures.
• Research recommendation system design patterns.

Resources I Recommend

• System design interview prep courses
• Books on distributed systems and machine learning

Common Pitfalls to Avoid

• Lack of experience in specific system design domains can be a significant disadvantage.
• Spending too much time on one aspect of system design (e.g., service count) at the expense of others (e.g., features, modeling).

LeetCode similar: LeetCode K Closest Points to Origin