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 Axon.

What role is this question designed for?

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

Design Video Intelligence for Investigations

Last updated: May 30, 2026

Quick Overview

This question evaluates a candidate’s ability to design scalable, privacy-aware machine learning systems for large-scale video understanding and retrieval, assessing competencies in system architecture, data pipelines, information retrieval and indexing, model evaluation, and mitigation of risks like hallucination, bias, and incorrect evidence attribution. Commonly asked to probe architectural trade-offs, operational and legal constraints, and the integration of ML components with investigative search workflows, it is in the ML System Design category and tests both conceptual understanding and practical application.

Axon

May 26, 2026, 12:00 AM

Machine Learning Engineer

Technical Screen

ML System Design

Design a law-enforcement video intelligence system for investigators working with very large video archives. Input videos may come from body-worn cameras, CCTV, dashboard cameras, interview-room cameras, drones, or uploaded evidence clips.

The goal is not merely to summarize each video. The system should help investigators search, reason over, and locate evidence-relevant moments across massive archives.

Describe the product requirements, ML components, data pipeline, indexing and retrieval strategy, system architecture, evaluation metrics, reliability considerations, privacy and compliance controls, and how you would mitigate safety risks such as hallucination, bias, and incorrect evidence attribution.

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