Katkat
Back

Design Detection Systems for Risk and Safety

PinterestMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

Last updated

This question tests the ability to design an end-to-end classification system for detecting harmful content (e.g., spam or toxic images) while balancing precision and recall at scale.

Problem: Designing a Content Safety Classifier

Pinterest hosts billions of pins. Design an ML system to detect and flag "Harmful Content" (e.g., spam, phishing, or inappropriate imagery) in real-time as users upload or share pins.

Requirements:

  1. Functional: Automatically classify content as "Safe" or "Harmful." Handle both image and metadata (text descriptions, link URLs).
  2. Non-Functional:
    • Latency: The classification must happen within 200ms of the upload request.
    • Scale: The system must support thousands of queries per second (QPS).
    • Accuracy: High precision is required to avoid punishing legitimate users (false positives are costly).

The solution should address:

  • Data Strategy: How do you handle class imbalance (harmful content is rare) and obtain ground truth labels?
  • Model Architecture: Describe the pipeline, including feature extraction (text vs. image embeddings) and model selection.
  • Serving: How do you integrate the model into the existing upload flow, and what happens if the model is slow or unavailable?
  • Feedback Loop: How do you incorporate user reports and moderator decisions into the system?
  • Anti-Abuse: How do you prevent bad actors from "probing" the model to find blind spots?

Hints

Hint 1

Consider using a multi-stage approach where a lightweight model filters out obvious spam before passing complex cases to a heavier deep learning model.

Hint 2

Think about how to leverage multi-modal embeddings (e.g., CLIP) to correlate suspicious text with suspicious image patterns.

Hint 3

Address the cold-start problem by discussing how to handle content from new users versus established, trusted accounts.

Follow-up questions

  • How would you measure the performance of your system in production beyond just accuracy?
  • If an attacker starts generating adversarial images designed to bypass your model, how would you iterate your training data to build resistance?
  • How would you implement a 'Human-in-the-loop' strategy to ensure your training data stays representative of shifting user behavior?