Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Meta ML Engineer interview focused on a classification problem around harmful item detection. I came prepared, delivered everything I had, but the interviewers seemed checked out the whole time and barely asked follow-ups. Hard to read how it went when you're basically talking into a void.

Questions Asked (1)

Design a machine learning system to detect harmful items.

System DesignTechnical Trade-offsProduct Sense & Ideation

Author's notes

I had a whole prepared answer for this and went through all of it.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the scope: what types of harmful items (e.g., text, images, videos) and what platform (e.g., Facebook Marketplace, Instagram). Then outline a high-level system design covering data collection, model training, deployment, and monitoring, while discussing trade-offs between precision and recall, latency, and scalability.

Pro tip: Emphasize the importance of human-in-the-loop and continuous learning: harmful content evolves, so the system must adapt quickly. Also, discuss how to handle adversarial attacks and edge cases.

1. Clarify Requirements and Scope

Ask questions to understand what 'harmful items' means (e.g., weapons, drugs, hate speech), the modalities (text, image, video), scale (millions of posts per day), and latency requirements (real-time vs. batch).

2. Data Collection and Labeling

Discuss sourcing labeled data: historical user reports, human review, synthetic data, and active learning. Address challenges like class imbalance, noisy labels, and privacy.

3. Model Selection and Training

Choose appropriate models (e.g., CNN for images, transformer for text, multimodal for combined). Consider ensemble methods, transfer learning, and handling adversarial examples. Discuss evaluation metrics (precision, recall, F1, AUC) and trade-offs.

4. Deployment and Scaling

Design a scalable serving architecture (e.g., microservices, batch processing, real-time inference). Discuss model compression, caching, and load balancing. Address latency and throughput requirements.

5. Monitoring and Iteration

Set up monitoring for model drift, performance degradation, and false positives/negatives. Implement feedback loops with human reviewers and retraining pipelines. Discuss A/B testing and rollout strategies.

Key Points to Mention

Trade-off between precision and recall: high recall may increase false positives, impacting user experience; high precision may miss harmful items.
Handling multimodal data: combining text, image, and video signals for better detection.
Adversarial attacks: how to make models robust to evasion techniques.
Human-in-the-loop: using human reviewers for ambiguous cases and to generate training data.
Scalability: designing for billions of daily posts with low latency.
Ethical considerations: bias, privacy, and transparency in automated moderation.

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