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 Technical Screen 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 a Short-Video Recommendation System

Last updated: Mar 29, 2026

Quick Overview

This question evaluates competency in designing large-scale short-video recommendation systems, including machine learning model selection, candidate generation and ranking, real-time personalization, feedback signal design, evaluation metrics, latency and scalability constraints, cold-start handling, exploration–exploitation trade-offs, and safety/abuse controls. It is commonly asked in the ML system design domain to assess system-level machine learning engineering and product-aware architectural thinking, and it combines conceptual understanding with practical application by requiring both high-level trade-off reasoning and concrete serving and evaluation considerations.

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