Designing a Web-Scale Data Mining and Curation System for Multimodal LLMs

Modern multimodal assistants are not bottlenecked by model architecture alone. Increasingly, the real competitive advantage comes from the ability to continuously discover, filter, rank, annotate, and integrate high-quality training data from massive noisy corpora.

This system design question appears frequently in senior ML infrastructure and applied AI interviews because it probes far more than algorithmic knowledge. It tests whether a candidate can reason about:

• iterative data flywheels,
• scalable ML infrastructure,
• human-in-the-loop systems,
• multimodal retrieval pipelines,
• and evaluation-driven dataset construction.

In this article, we reconstruct and deeply analyze a canonical version of this interview question.