Meta — Software Engineer ✅ Passed
Level: Unknown Level
Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 240 min · Interviewer: Unfriendly
Topics: Artificial Intelligence, Maze Algorithm, LRU Cache, Advertising Ranking, Feature Engineering, Model Architecture, Calibration, Behavioral Questions
Location: Menlo Park, CA
Interview date: 2026-01-10
Summary
Round 1: Coding (AI Coding)
Question: Maze problem with AI assistance. It was similar to commonly asked questions.
Round 2: Coding
Question: LeetCode 138 and LeetCode 146 (LRU Cache), both frequently asked.
Round 3: Machine Learning System Design (ML SD)
Question: Ads Ranking. Discussed feature engineering, model architecture, and calibration.
Round 4: Behavioral (BQ)
Question: Standard behavioral questions, mainly about conflict with peers and project challenges.
Details
Preparation Tips & Key Takeaways
What I Learned
- I needed to focus on how AI can assist in solving coding problems, not just the algorithm itself.
- I realized the importance of thoroughly understanding common data structures like LRU Cache.
- I learned to prepare for system design interviews by studying ad ranking systems, feature engineering, model architecture, and calibration.
- I understood the necessity of practicing standard behavioral questions, especially those related to conflict resolution and project challenges.
Recommended Preparation
Coding Practice
- Practice maze algorithms and focus on AI-assisted approaches.
- Thoroughly understand and practice LRU Cache implementation.
System Design
- Study advertising ranking systems.
- Review feature engineering, model architecture, and calibration techniques.
Behavioral Questions
- Prepare STAR stories for conflict resolution and project challenges.
- Practice answering standard behavioral questions.
Resources I Recommend
- Online resources for maze algorithms and AI-assisted problem solving.
- LeetCode for practicing LRU Cache implementation.
- Case studies on advertising ranking systems.
Common Pitfalls to Avoid
- Neglecting the AI aspect of AI-enabled coding problems.
- Underestimating the importance of common data structures like LRU Cache.
- Lack of preparation for system design questions related to advertising ranking.
LeetCode similar: LeetCode 138, LeetCode 146