Machine Learning Engineer · Onsite · Multiple Types

Meta — Machine Learning Engineer ✅ Passed

Level: Mid-Level

Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 240 min · Interviewer: Unfriendly

Topics: Coding, Minimum in list, Lowest Common Ancestor, AI Enabled Coding, Maze Algorithm, Machine Learning System Design, Weapon Sales Ads Detection, Behavioral Questions, Data Collection, Funnel Model, Training Data

Location: Menlo Park, CA

Interview date: 2026-01-14

Summary

Round 1: Coding

Question: Finding the minimum value in a list (a variation of LeetCode 162). Finding the lowest common ancestor. Both were frequently asked and relatively simple questions. I didn't perform as smoothly as I'd hoped.

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Round 2: AI Enabled Coding

Question: Maze problem. The first task was to write a function that prints a path. Given a list of path points (including entrance and exit), I needed to modify the maze by changing the points on the path to '*' characters, while keeping the entrance 'e' and exit 'E' unchanged. The output was the modified maze.

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Round 3: Machine Learning System Design (ML SD)

Question: Designing a weapon sales ads detection system, a commonly asked question. The interviewer asked questions about collecting training data and whether the training data for the funnel model was the same. They also provided feedback throughout the round.

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Round 4: Behavioral (BQ)

Question: Standard behavioral questions, such as describing a project I was proud of, an ambiguous project, a project that changed midway, constructive feedback I gave and received, and my experience managing others. The interviewer asked many follow-up questions.

Details

Preparation Tips & Key Takeaways

What I Learned

• The importance of maintaining a good mindset during interviews and not psyching myself out.
• Recognizing the high degree of overlap in interview questions, emphasizing the value of studying frequently asked questions.

Recommended Preparation

Coding Practice

• Focus on frequently asked coding questions.
• Practice tree-based algorithms and maze traversal problems.

System Design

• Review common system design patterns, specifically for ad detection systems.
• Understand data collection strategies and funnel model training.

Behavioral Questions

• Prepare STAR stories for common behavioral questions like project accomplishments, challenges, and feedback experiences.
• Practice elaborating on details and anticipating follow-up questions.

Resources I Recommend

• Reviewing interview experiences shared online to identify frequently asked questions.
• Practicing coding problems on platforms like LeetCode.

Common Pitfalls to Avoid

• Getting flustered by simple coding problems.
• Underestimating the depth of follow-up questions in behavioral rounds.

LeetCode similar: LeetCode 162