Spent the first few minutes going too broad, talking about rule-based filters before the interviewer nudged me toward the ML side of things.
Start by clarifying the product requirements and scale (e.g., billions of comments, multiple languages, real-time moderation). Then propose a multi-stage ML pipeline that balances precision, recall, and latency, and discuss trade-offs between automated and human review.
Pro tip: Emphasize the importance of a feedback loop where human moderator decisions continuously retrain and improve the models, and discuss how to measure and mitigate false positives to avoid over-censorship.
Ask questions to understand the volume, languages, content types, latency requirements, and definition of harmful content. Establish success metrics like precision, recall, and user impact.
Outline a multi-stage system: fast filtering (e.g., keyword/regex), lightweight classifiers for triage, and heavy models for nuanced decisions. Include components for text, image, and video moderation.
Discuss data collection, labeling (including human-in-the-loop), handling class imbalance, and techniques for multilingual and multimodal moderation. Mention active learning and continuous retraining.
Explain how to balance precision vs. recall, latency vs. accuracy, and automation vs. human review. Discuss adversarial attacks, context-dependent content, and appeals.
Propose offline metrics (e.g., F1, AUC) and online metrics (e.g., user reports, moderator efficiency). Describe A/B testing, shadow deployment, and monitoring for drift and bias.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.