Design Detection Systems for Risk and Safety
Company: Pinterest
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
The machine learning system design rounds focused on designing end-to-end production systems for several detection problems:
1. **Bank fraud detection**: detect fraudulent transactions in near real time while minimizing false declines for legitimate users.
2. **Harmful content detection for trust and safety**: identify policy-violating or low-quality content such as spam, scams, explicit material, hate speech, or other unsafe posts using content and account signals.
3. **Landing-page failure detection**: detect when a linked or advertised destination returns 404, times out, or is otherwise offline.
For each prompt, explain:
- the product goal and what action the system should take
- labels and data collection
- important features and representations
- model choice and training pipeline
- online serving architecture and latency requirements
- thresholding, human review, and feedback loops
- evaluation metrics and tradeoffs
- monitoring, drift detection, and abuse resistance
Quick Answer: This question evaluates a machine learning engineer's competence in designing end-to-end detection systems for risk and safety, covering skills in data and label strategy, feature representation, model training and selection, real-time serving, thresholding and human-in-the-loop feedback, monitoring, and abuse resistance.