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 Onsite rounds at OpenAI.

What role is this question designed for?

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

Select high-quality math documents from crawls

Last updated: Mar 29, 2026

Quick Overview

This ML System Design question evaluates the ability to design scalable, production-grade pipelines for extracting and quality-scoring mathematical content from heterogeneous formats (HTML, PDF, scanned images), exercising concepts such as information extraction and OCR, document scoring and deduplication, licensing and safety enforcement, and operational monitoring. It is commonly asked because real-world applications must handle noisy, web-crawled data at scale while delivering measurable quality metrics and human review workflows, making it a practical probe of high-level system architecture, modeling trade-offs, and production operations.

OpenAI

Dec 15, 2025, 12:00 AM

Machine Learning Engineer

Onsite

ML System Design

Scenario

You have a web crawler that collects raw HTML/PDF documents. You want to build a pipeline that identifies high-quality math documents suitable for downstream use (e.g., search, dataset creation, or training).

Task

Design an end-to-end system to:

Extract math content from crawled pages.
Score and filter documents for quality.
Deduplicate and enforce licensing/safety constraints.

Requirements

Handle HTML, PDF, and scanned images.
Favor documents with substantial, correct mathematical content (not spam or low-effort copies).
Scale to tens/hundreds of millions of documents.
Provide measurable quality metrics and a human review loop.

Deliverables

Architecture, key features/signals, modeling approach, evaluation, and operations (monitoring, drift, reprocessing).

Solution

Show

Submit Your Answer to Earn 20XP

Loading comments...

Browse More Questions

More ML System Design•More OpenAI•More Machine Learning Engineer•OpenAI Machine Learning Engineer•OpenAI ML System Design•Machine Learning Engineer ML System Design