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Design a Trustworthy Ranking System

SnapchatMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

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This problem tests the ability to design an end-to-end ML system for ranking content while balancing engagement metrics with safety, misinformation, and user trust.

Design a Trustworthy Ranking System

You are tasked with designing a ranking service for a social media discovery feed that ensures content is both engaging and trustworthy. The system must filter out low-quality, harmful, or misleading content without significantly degrading the user experience.

Requirements

  • Functional: Rank posts in a user's feed such that high-trust, relevant content is prioritized.
  • Non-Functional: Low latency (under 100ms for ranking), ability to handle high throughput, and robust mechanisms to prevent the spread of misinformation.
  • Constraints: You have access to user engagement signals (clicks, shares, time spent) and trust signals (user reports, moderator labels, source credibility scores).

What to Cover

  1. High-Level Architecture: Describe the pipeline from candidate generation to final re-ranking.
  2. Trust Signals: Define how you would represent 'trust' quantitatively. How do you handle cold-start content or new publishers?
  3. Optimization Strategy: Given the trade-off between engagement (e.g., clicks) and trustworthiness, how would you define your objective function?
  4. Feedback Loops: Explain how the system avoids creating 'echo chambers' or penalizing legitimate content based on biased reporting.
  5. Evaluation: How do you measure the success of the system offline and online?

Hints

Hint 1

Consider using a multi-stage funnel where an initial retrieval stage filters for high-trust sources before complex ranking takes place.

Hint 2

Think about using a multi-objective loss function where engagement and trust are weighted factors rather than trying to optimize for a single scalar.

Hint 3

Propose a 'calibration' layer to account for reporting bias, where reports from high-trust users are weighted more heavily than reports from bot-like or bad-actor accounts.

Follow-up questions

  • How would you handle a sudden viral misinformation campaign that the current labels have not caught yet?
  • How does your system design change if you need to explain to a user why a specific piece of content was deemed low-trust?
  • What monitoring metrics would you track in production to detect model drift or a sudden drop in content diversity?