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Pinterest·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior
Apr 2026

Summary

Pinterest ML Engineer interview focused almost entirely on a single deep system design question about content moderation at scale. It was a long, sprawling session and I left unsure whether I'd covered enough ground or just rambled for an hour.

Questions Asked (1)

Q1

Design an ML system to detect unsafe content (violent, sexual, hateful, harassing, or policy-violating posts, images, and videos) on a large user-generated-content platform.

System DesignTechnical Trade-offsProduct Analytics & Metrics
Author's notes

This thing sprawled in every direction.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and requirements: types of unsafe content, modalities (text, image, video), scale, latency, and precision-recall trade-offs. Then propose a multi-stage system with fast filtering, ML models for each modality, and human review for edge cases, emphasizing metrics and iteration.

Pro tip: Emphasize the importance of defining clear, measurable metrics (e.g., precision at high recall) and designing for feedback loops where human decisions continuously improve models. Also, discuss how to handle adversarial evasion and concept drift.

1. Clarify Requirements and Constraints

Ask questions to understand the scale (e.g., millions of posts per day), latency requirements (real-time vs. batch), content types (text, image, video), and the cost of false positives vs. false negatives. Also, consider policy definitions and regional variations.

2. Design a Multi-Stage Architecture

Propose a pipeline: (1) fast, lightweight filters (e.g., hash matching, keyword lists) to catch obvious violations; (2) ML models for each modality (text, image, video) to score content; (3) a fusion layer to combine signals; (4) human review for borderline cases. This balances efficiency and accuracy.

3. Select Models and Features

For text: use transformer-based models (e.g., BERT) fine-tuned on labeled data. For images: use CNN or vision transformers. For video: sample frames and apply image models, plus audio analysis if needed. Incorporate metadata and user signals as features.

4. Define Metrics and Evaluation

Choose metrics like precision, recall, F1, and AUC, but prioritize high recall to catch violations, with precision managed via human review. Use a held-out test set and monitor online metrics like user reports and appeal rates. Consider business metrics like user trust and engagement.

5. Plan for Deployment and Iteration

Discuss serving infrastructure (e.g., batch vs. real-time), model versioning, A/B testing, and feedback loops where human labels retrain models. Address adversarial attacks and drift with continuous monitoring and retraining.

Key Points to Mention

  • Multi-modal approach: separate models for text, image, and video, with a fusion mechanism.
  • Trade-off between precision and recall: high recall to minimize harmful content, with human review to control false positives.
  • Use of pre-trained models and transfer learning to reduce labeling needs.
  • Human-in-the-loop: active learning and feedback to improve models over time.
  • Scalability: distributed processing, caching, and efficient model inference (e.g., quantization, distillation).
  • Metrics: offline (precision, recall, AUC) and online (user reports, appeal rates, prevalence).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.