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

Senior
Jun 2026

Summary

Did a design round at Meta for an ML Engineer role, focused on weapons advertising. The whole thing centered on data collection questions, and while AI tools were supposedly available, the interviewer never turned them on so that was kind of a non-factor.

Questions Asked (1)

Q1

How would you approach data collection for a weapons advertising system?

System DesignProduct Analytics & MetricsTechnical Trade-offs
Author's notes

This was the bulk of the round.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the business objective and constraints of the weapons advertising system, then outline a data collection strategy that balances relevance, compliance, and scalability. Structure your answer around defining requirements, identifying data sources, designing collection mechanisms, and addressing ethical/legal considerations.

Pro tip: Acknowledge the sensitive nature of weapons advertising and emphasize compliance with Meta's policies and legal regulations; show that you prioritize responsible AI and user safety alongside technical feasibility.

1. Clarify Objectives and Constraints

Ask questions to understand the goal: is it to target specific audiences, measure ad effectiveness, or optimize content? Identify constraints like legal restrictions, platform policies, and ethical boundaries.

2. Identify Data Sources

Determine what data is needed: user demographics, interests, behavior, ad content, and context. Consider internal sources (user profiles, ad interactions) and external sources (public records, partner data) while respecting privacy.

3. Design Collection Mechanisms

Outline how data will be collected: real-time event logging, batch processing, surveys, or third-party APIs. Ensure data quality, volume, and velocity are handled with appropriate tools (e.g., Kafka, Spark).

4. Address Compliance and Ethics

Detail how you will comply with regulations (GDPR, CCPA), platform policies, and ethical guidelines. Implement consent management, anonymization, and auditing to prevent misuse.

5. Plan for Validation and Iteration

Describe how you will validate data quality, monitor for bias, and iterate based on feedback. Set up metrics to evaluate the effectiveness of the data collection system.

Key Points to Mention

  • Data privacy and user consent mechanisms
  • Regulatory compliance (e.g., GDPR, CCPA, Meta's advertising policies)
  • Scalable data pipelines for real-time and batch processing
  • Bias detection and fairness in ad targeting
  • Integration with existing ML infrastructure for feature engineering
  • Ethical considerations specific to weapons advertising

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