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Meta·Product Manager·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
May 2026

Summary

Meta PM interview focused entirely on FB Locals, a local business review product. Both questions came from the same scenario but hit very different angles, one defensive and one adversarial, which I wasn't really prepared for.

Questions Asked (2)

Q1

You're the PM for FB Locals, a product that lists reviews of local businesses. How would you approach authenticating reviews, and what would you build?

Product Sense & IdeationProduct StrategyTechnical Trade-offs
Author's notes

I went straight to verification signals like account age, purchase history, check-in data.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the goal: authentic reviews build trust and help users make decisions, while fake reviews harm the platform. Then outline a multi-layered authentication strategy combining proactive detection, reactive verification, and user reporting, and propose specific features to build, prioritizing by impact and feasibility.

Pro tip: Frame authentication as a trust and safety problem, not just a technical one, and emphasize that the solution must balance friction for genuine users with deterrence for bad actors. Also, consider leveraging Meta's unique assets like social graph and AI capabilities.

1. Clarify goals and constraints

Define what 'authentic' means: reviews from real customers with genuine experiences. Identify key metrics: % fake reviews, user trust, review volume, and business impact. Consider constraints like privacy, scalability, and user friction.

2. Identify types of inauthentic reviews

Map out common fake review patterns: incentivized reviews, competitor sabotage, bot-generated reviews, and biased reviews from friends/family. Understand the motivations behind each to design targeted solutions.

3. Design multi-layered authentication approach

Propose a combination of proactive measures (e.g., ML models to detect anomalies, social graph analysis, purchase verification) and reactive measures (e.g., user reporting, business responses, manual moderation). Prioritize based on impact and feasibility.

4. Prioritize features to build

Select 2-3 high-impact features to build first, such as requiring check-in or transaction verification, implementing a reputation system for reviewers, or using AI to flag suspicious patterns. Explain how each addresses specific fake review types.

5. Define success metrics and iterate

Establish metrics to measure effectiveness: reduction in fake reviews, increase in user trust (surveys), review helpfulness scores, and impact on business listings. Plan for continuous monitoring and iteration.

Key Points to Mention

  • Leverage Meta's social graph to verify reviewer authenticity (e.g., account age, friend connections, activity history).
  • Use machine learning to detect anomalies in review patterns (e.g., sudden spikes, duplicate content, IP clustering).
  • Implement verification methods like requiring a check-in, receipt upload, or integration with payment partners.
  • Design a reputation system for reviewers to incentivize authentic contributions and penalize bad actors.
  • Balance friction: too much verification may deter genuine users; too little may allow fakes.
  • Consider privacy implications and ensure compliance with data protection regulations.

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

Q2

Now flip it: you're a small business in an emerging market and you're building a fake review operation targeting FB Locals. What do you build and what's your process?

Product StrategyAdaptability & AmbiguityProduct Sense & Ideation
Author's notes

This one threw me.

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AI HintsAI Generated

Suggested Approach

Adopt the adversary's mindset to reverse-engineer the attack: define the goal (e.g., boost local rankings or damage a competitor), then design the minimal viable fake review operation using available tools and incentives. Walk through the full lifecycle—from account creation to review posting and evasion—while highlighting where Meta's defenses could be exploited or strengthened.

Pro tip: Focus on the economics and scalability of the operation: fake reviews are a volume game, so emphasize how you'd acquire accounts cheaply, automate posting, and avoid detection. This shows you understand both the attacker's constraints and Meta's detection surface.

1. Define the objective and constraints

Clarify the business goal (e.g., increase visibility, sabotage a rival) and the constraints (budget, technical skill, local labor, platform policies). This frames the entire operation.

2. Design the fake review system

Outline the components: account creation (e.g., purchased or farmed profiles), review generation (AI or manual), posting mechanisms (bots or human workers), and targeting (specific businesses or categories).

3. Map the operational process

Detail the end-to-end workflow: sourcing accounts, warming them up, writing/posting reviews, scaling volume, and rotating tactics to avoid detection. Include timing and coordination.

4. Identify evasion techniques

Explain how you'd avoid Meta's detection: using diverse IPs, mimicking organic behavior, spacing out reviews, and leveraging local cultural nuances to appear authentic.

5. Assess impact and countermeasures

Evaluate the potential success and risks, then suggest how Meta could detect and mitigate such operations (e.g., improved anomaly detection, verification requirements).

Key Points to Mention

  • Incentives for fake reviews: low cost of account creation, high impact on local search rankings, and difficulty of detection in emerging markets.
  • Account sourcing: purchased accounts, SIM farms, or incentivized real users (e.g., micro-payments).
  • Review generation: AI-generated text, templated reviews, or crowdsourced from local workers to mimic authentic language.
  • Posting mechanisms: automation via bots, manual posting by workers, or leveraging APIs if accessible.
  • Evasion tactics: IP rotation, gradual review posting, using aged accounts, and avoiding patterns like identical timestamps.
  • Meta's countermeasures: machine learning detection, user reporting, business verification, and rate limiting.

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