Machine Learning Engineer · Onsite · Multiple Types

Pinterest — Machine Learning Engineer ❌ Failed

Level: Senior-Level

Round: Onsite · Type: Multiple Types · Difficulty: 7/10 · Duration: 300 min · Interviewer: Unfriendly

Topics: Machine Learning Design, Data Structures (Trie), Algorithms, Behavioral Questions, Leadership Principles

Location: San Francisco Bay Area

Interview date: 2026-01-19

Got offer: False

Summary

Round 1: ML Design

Question: Design a system to detect unsafe content. I spent too much time on this and almost ran out of time.

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Round 2: ML Design

Question: Design a real-time home feed that is responsive to user engagement. The interviewer seemed to have a strong infrastructure background and asked about specific service implementation details, which I struggled with.

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Round 3: Coding

Question: Implement a Trie for search auto-completion. I spent a long time writing the code, but it had bugs. I couldn't figure out where the errors were, and the interviewer suggested I try a different approach. I realized later that I had defined the Trie node as a class variable. Fixing that would have made it work.

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Round 4: Coding

Question: Given several rooms and people, initially all in room 0, and a function move(person) that moves a person to the next room. Given a room number, how many people are inside it (requiring O(1) time)? Output the k people who moved the fastest. For example, 5 people and 5 rooms, move(1), move(2), move(1). Room 1 has one person, and Room 2 has one person. The top 2 are person 1 and person 2. The suggestion of using a doubly linked list for each room (because the move fast part needs to consider people in the same room, starting from the earliest to enter the room) was accepted, but the implementation still had bugs. The interviewer helped debug for a long time, but after 15 minutes of overtime, the issue was still not found.

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Round 5: Behavioral (BQ)

Question: Standard behavioral questions, divided into leadership and technical aspects. The technical questions were quite detailed. When I mentioned a concept, the interviewer asked me to provide a specific example.

Details

Preparation Tips & Key Takeaways

What I Learned

• System design questions can be very open-ended and require a broad understanding of infrastructure.
• Debugging under pressure is a critical skill. I need to practice identifying common errors quickly.
• Behavioral questions can be very specific and require detailed examples.

Recommended Preparation

Coding Practice

• Practice implementing data structures like Tries.
• Focus on debugging techniques and common coding errors.

System Design

• Study system design principles for real-time systems.
• Review common design patterns for handling user engagement and content delivery.

Behavioral Questions

• Prepare STAR stories that demonstrate leadership and technical skills.
• Practice providing specific examples to illustrate technical concepts.

Resources I Recommend

• LeetCode for coding practice.
• System design resources for real-time systems.
• Mock interviews for behavioral questions.

Common Pitfalls to Avoid

• Spending too much time on a single question.
• Failing to debug code under pressure.
• Providing vague or generic answers to behavioral questions.