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

SeniorPrefer not to say

May 2026

Summary

Meta system design round, one question about building a content moderation pipeline for offensive comments. Pretty open-ended and I felt like I was rambling for the first few minutes before finding a direction.

Questions Asked (1)

Design a system to automatically detect and filter offensive comments at scale.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

I jumped straight into the ML pipeline and the interviewer had to pull me back to ask about ingestion and latency requirements first.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then propose a multi-stage pipeline combining ML models, rule-based filters, and human review. Emphasize trade-offs between precision/recall, latency, and cost, and discuss how to handle adversarial evasion and feedback loops.

Pro tip: Show you understand that perfect detection is impossible; instead, focus on designing a system that balances false positives and false negatives based on product goals and user impact, and include mechanisms for continuous learning from human moderation.

1. Clarify Requirements and Scale

Ask about scale (e.g., comments per second), latency requirements, definition of offensive content, and tolerance for false positives/negatives. This sets the stage for design decisions.

2. High-Level Architecture

Outline a pipeline: ingestion, preprocessing, classification (ML + rules), decision layer, and action (filter, flag, allow). Include feedback loops for model improvement.

3. Model and Feature Design

Discuss model choices (e.g., fine-tuned transformers, lightweight models for scale), features (text, user history, context), and handling multilingual and adversarial inputs.

4. Trade-offs and Optimization

Analyze trade-offs: precision vs. recall, latency vs. accuracy, cost vs. performance. Propose tiered approach: fast filter for obvious cases, heavy model for ambiguous, human review for edge cases.

5. Evaluation and Iteration

Define metrics (precision, recall, F1, latency, cost per decision), offline/online evaluation, A/B testing, and continuous learning from human labels and user reports.

Key Points to Mention

Multi-stage pipeline: cheap filters first, then ML models, then human review for ambiguous cases.
Use of pre-trained language models (e.g., BERT) fine-tuned on offensive language datasets, with considerations for multilingual support.
Handling adversarial evasion: obfuscation, misspellings, context-dependent offenses; use of character-level features and adversarial training.
Trade-offs: precision vs. recall (false positives can silence legitimate speech, false negatives harm users), latency vs. accuracy, cost vs. scale.
Feedback loops: human moderation labels, user reports, and active learning to continuously improve models.
Scalability: distributed processing (e.g., Kafka, Spark), model serving with low latency (e.g., TensorFlow Serving), caching, and sharding.

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