Design comment ranking for a news feed
Overview
This question evaluates a candidate's ability to design an ML-powered comment-ranking system, testing competencies in personalization, engagement and quality modeling, data and label definition, online serving, and robustness to abuse and latency constraints within the ML System Design domain.
Meta
Dec 15, 2025, 12:00 AM
Machine Learning Engineer
Onsite
ML System Design
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Design an ML-powered system to rank comments under posts in a news feed product.
Requirements
- For each feed item (post/story), users can open the comment section and should see an ordered list of comments.
-
Ranking should be
personalized
(depends on the viewer) and should balance:
- relevance/engagement (e.g., likes, replies, dwell time),
- quality (avoid spam/toxicity/low-effort),
- freshness (new comments can surface),
- author/viewer relationships.
Constraints / considerations
- High QPS and strict latency (assume p95 end-to-end budget ~100–200 ms for the comment list).
- Support new comments arriving continuously.
- Handle abuse (spam, brigading, adversarial behavior), deleted comments, and block lists.
- You should describe: data, labels, modeling approach, online serving architecture, evaluation (offline + A/B), and monitoring/guardrails.
Solution
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