I jumped straight into a classifier pipeline and the interviewer kind of just waited.
Start by clarifying requirements and scale, then propose a multi-layered system combining rule-based filters, ML classifiers, and human review. Emphasize trade-offs between precision, recall, latency, and cost, and describe how to handle evolving policies and adversarial sellers.
Pro tip: Frame the problem as a precision-recall trade-off: false negatives (bad listings live) harm trust and may be illegal, while false positives (good listings blocked) hurt sellers and revenue. Propose a tiered enforcement strategy with appeals and feedback loops to continuously improve.
Ask about listing volume, types of restricted items, latency needs, and integration points. Define success metrics like precision, recall, and time-to-takedown.
Propose a pipeline: rule-based filters (keywords, image hashes), ML models (text, image, multimodal), and anomaly detection. Include a human review queue for ambiguous cases.
Discuss training data sourcing, labeling, and handling class imbalance. Cover model retraining, drift detection, and adaptation to new restricted categories.
Specify actions: auto-block, flag for review, or allow with monitoring. Include seller appeals, user reporting, and using feedback to improve models.
Analyze trade-offs between precision/recall, latency, cost, and seller experience. Describe how to scale horizontally and handle peak loads.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.