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 hard difficulty ML System Design question, commonly asked during Technical Screen rounds at OpenAI.

What role is this question designed for?

This question is commonly asked for Software Engineer candidates at OpenAI during technical interviews.

Design an End-to-End ML System | OpenAI Interview Question

Quick Overview

This question evaluates a candidate's competency in designing end-to-end machine learning systems for real-time recommendation services within the ML System Design domain, covering data collection and event pipelines, feature engineering and stores, model training and retraining, online serving architecture, monitoring, and operational constraints.

System Design: Real-Time Recommendation ML System

Context

You are tasked with designing an end-to-end machine-learning system that serves real-time recommendations in a consumer-facing product (e.g., feed, products, videos). The system must handle high read traffic and evolving content and user behavior.

Assumptions (you may refine during the interview):

Traffic: ~10k QPS; p95 latency target ≤ 150 ms for recommendation API
Inventory: 10M items; daily new/expiring items
Feedback: clicks, likes, purchases; implicit and explicit signals
Privacy: user consent, PII minimization, right-to-erasure compliance

Requirements

Explain and justify the design for each of the following:

Data collection and event pipeline
Feature engineering and feature store (offline and online)
Model training, labeling, and retraining strategy
Online serving architecture (candidate generation, ranking, re-ranking)
Monitoring, alerting, and experimentation
Scalability, reliability, and cost considerations

Quick Overview

Context

Assumptions (you may refine during the interview):

Traffic: ~10k QPS; p95 latency target ≤ 150 ms for recommendation API

Inventory: 10M items; daily new/expiring items

Feedback: clicks, likes, purchases; implicit and explicit signals

Privacy: user consent, PII minimization, right-to-erasure compliance

Requirements

Explain and justify the design for each of the following:

Data collection and event pipeline

Feature engineering and feature store (offline and online)

Model training, labeling, and retraining strategy

Online serving architecture (candidate generation, ranking, re-ranking)

Monitoring, alerting, and experimentation

Scalability, reliability, and cost considerations

Design an End-to-End ML System

Quick Overview

System Design: Real-Time Recommendation ML System

Context

Requirements

Solution

Submit Your Answer

Design an End-to-End ML System

Quick Overview

System Design: Real-Time Recommendation ML System

Context

Requirements

Solution

Submit Your Answer