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Meta·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Apr 2026

Summary

Meta system design interview, one question about deduplicating school entities on the platform. Pretty open-ended and I wasn't totally sure how deep to go on the data side versus the product side.

Questions Asked (1)

Q1

Design a system to detect and filter duplicate school entries on Facebook.

System DesignData ModelingTechnical Trade-offs
Author's notes

I started with fuzzy string matching on school names and then realized there's a whole mess of location data, alternate names, and user-generated variations to deal with.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining what constitutes a duplicate school entry, then propose a scalable architecture that combines rule-based matching with machine learning for fuzzy matching, and finally discuss trade-offs between precision and recall, latency, and cost.

Pro tip: Emphasize the importance of a human-in-the-loop review process for edge cases and continuously improving the model with feedback to balance automation and accuracy.

1. Clarify Requirements

Ask questions to understand scale, data sources, definition of duplicate, and business impact of false positives/negatives.

2. Data Modeling and Feature Extraction

Design a schema to store school attributes and extract features like name, location, website, and admin details for comparison.

3. Duplicate Detection Pipeline

Outline a multi-stage pipeline: candidate generation using blocking/indexing, then pairwise comparison with rules and ML models to classify duplicates.

4. Filtering and Resolution

Define actions for detected duplicates: auto-merge, flag for review, or suggest merge to admins, with appropriate thresholds.

5. Scalability and Trade-offs

Discuss distributed processing, latency requirements, precision-recall trade-offs, and cost of false positives vs false negatives.

Key Points to Mention

  • Use of blocking techniques to reduce pairwise comparisons (e.g., by location or name phonetic hash).
  • Combination of deterministic rules (exact match on unique identifiers) and probabilistic matching (fuzzy string similarity, ML).
  • Handling of multilingual and variant school names (e.g., abbreviations, translations).
  • Feedback loop from user reports and admin actions to improve model.
  • Scalability considerations: batch vs real-time processing, use of MapReduce/Spark for large-scale similarity joins.
  • Privacy and policy considerations when merging school pages.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.