How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Meta during technical interviews.

Design comment ranking for a news feed

Last updated: Mar 29, 2026

Quick 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.

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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