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 Roblox.

What role is this question designed for?

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

Design a Static Audio Detection System | Roblox Interview Question

Quick Overview

This question evaluates system design and ML integration skills for building a scalable, reliable offline audio detection pipeline, including ingestion, preprocessing, STT and spectral feature extraction, rule-based post-processing, artifact persistence, and human-in-the-loop review within the ML system design domain.

System Design: Static Audio Detection Pipeline

Context

Design an offline (non-live) audio detection system that processes static audio files (e.g., user-uploaded clips) for policy compliance and quality. The goal is to ingest files, extract signals (speech-to-text, spectral features, keywords), combine them via rules, classify outcomes, and support human review where needed.

Requirements

Functional
- Ingest audio files from object storage.
- Preprocess (validation, transcoding, noise reduction, segmentation).
- Extract features: spectral analysis, speech-to-text (STT), keyword/phrase detection.
- Combine signals using a rule-based post-processor to classify each asset as: Clean, Problematic, or Needs Human Review.
- Persist artifacts (features, transcript, decisions) and expose results via API/stream.
- Discover new files automatically; support both event-driven and scheduled/batch discovery.
- Provide a manual review workflow (assignment, labeling, consensus, requeueing, audit).
- Support reprocessing/backfill when rules change.
Non-Functional
- Scalability: handle large daily volumes with predictable throughput.
- Latency: near-real-time (minutes) for most files.
- Reliability/fault tolerance: at-least-once processing, idempotent tasks, DLQs.
- Cost efficiency: optimize storage/compute and third-party API usage.
- Security/privacy: encryption at rest/in-transit, access controls, audit trails.
- Observability: metrics, logs, traces; quality and health monitoring.
Out of Scope
- Selecting or training ML models. Assume pluggable components.

Deliverables

Functional and non-functional requirements.
Key entities and data model.
High-level architecture (storage, compute, orchestration).
End-to-end processing flow from ingestion to output.
Integration of STT, spectral analysis, keyword detection, noise reduction, and rule-based post-processing.
File discovery strategy (event-driven vs cron/batch).
Outcome classification scheme and manual review workflow.
Scalability, throughput/latency targets, data retention, fault tolerance, backfill, and cost controls.
Success metrics and monitoring/alerting for quality and system health.

Quick Overview

Context

Requirements

Functional

Ingest audio files from object storage.
Preprocess (validation, transcoding, noise reduction, segmentation).
Extract features: spectral analysis, speech-to-text (STT), keyword/phrase detection.
Combine signals using a rule-based post-processor to classify each asset as: Clean, Problematic, or Needs Human Review.
Persist artifacts (features, transcript, decisions) and expose results via API/stream.
Discover new files automatically; support both event-driven and scheduled/batch discovery.
Provide a manual review workflow (assignment, labeling, consensus, requeueing, audit).
Support reprocessing/backfill when rules change.

Non-Functional

Scalability: handle large daily volumes with predictable throughput.
Latency: near-real-time (minutes) for most files.
Reliability/fault tolerance: at-least-once processing, idempotent tasks, DLQs.
Cost efficiency: optimize storage/compute and third-party API usage.
Security/privacy: encryption at rest/in-transit, access controls, audit trails.
Observability: metrics, logs, traces; quality and health monitoring.

Out of Scope

Selecting or training ML models. Assume pluggable components.

Deliverables

Functional and non-functional requirements.

Key entities and data model.

High-level architecture (storage, compute, orchestration).

End-to-end processing flow from ingestion to output.

Integration of STT, spectral analysis, keyword detection, noise reduction, and rule-based post-processing.

File discovery strategy (event-driven vs cron/batch).

Outcome classification scheme and manual review workflow.

Scalability, throughput/latency targets, data retention, fault tolerance, backfill, and cost controls.

Success metrics and monitoring/alerting for quality and system health.

Design a Static Audio Detection System

Quick Overview

System Design: Static Audio Detection Pipeline

Context

Requirements

Deliverables

Solution

Submit Your Answer to Earn 20XP

Design a Static Audio Detection System

Quick Overview

System Design: Static Audio Detection Pipeline

Context

Requirements

Deliverables

Solution

Submit Your Answer to Earn 20XP