Design a weapon-sale ad detection system
Company: Meta
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design an end-to-end ML system to detect and take action on ads/listings that attempt to sell weapons (or weapon-related prohibited items).
Your system should cover:
- Where detection happens (at creation time vs post-publication)
- What signals/modalities you use (text, images, seller behavior, etc.)
- How you collect and label training data
- How you evaluate and monitor the model
- How you integrate with enforcement (block, downrank, human review)
The interviewer may also ask:
1. How would you collect training data, especially for rare policy-violating content?
2. In a “funnel” setting (impression → click → conversion), is the training data the same for each stage/model? Why or why not?
Quick Answer: This question evaluates a candidate's competence in end-to-end machine learning system design, covering multimodal signal integration (text, images, behavior), data collection and labeling strategies, model evaluation and monitoring, and operational enforcement workflows for policy-violating content.