Design Smart Link Ranking

Product Context

Meta wants to improve a URL shortening product used across Facebook, Instagram, WhatsApp Business, and Ads Manager. Instead of only generating a short link, the system should use ML to decide the best redirect experience and destination variant for each click: for example deep-linking into an app, sending a user to a mobile web fallback, prioritizing a merchant landing page, or routing suspicious traffic to an interstitial.

Scale

Task

Design an end-to-end ML system for ranking redirect outcomes for a short-link platform at Meta scale.

1. Clarify the product objective and define the prediction target(s) for link creation time and click time.
2. Propose a multi-stage architecture for candidate generation, ranking, and final policy/re-ranking under a strict redirect latency budget.
3. Design the offline and online data pipelines, including labels, feature computation, and how you avoid training-serving skew.
4. Choose models for each stage and explain tradeoffs in accuracy, freshness, interpretability, and cost.
5. Define offline evaluation, online experimentation, and monitoring for drift, abuse, and system health.
6. Identify major failure modes and fallback behavior when models, features, or downstream services are unavailable.

Constraints

• Redirects are user-facing and latency-sensitive; p99 must stay below 80ms globally.
• Some clicks have limited identity context because of privacy settings, browser restrictions, or logged-out traffic.
• New links and new advertisers are common, so cold-start handling is required.
• The system must support abuse and spam controls; malicious links cannot be promoted by the ranker.
• Feature freshness matters: engagement and destination health signals should update within minutes, while heavier aggregations can be batch-computed daily.