Roblox·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

System design round at Roblox for a software engineer role, focused entirely on building a human-in-the-loop review pipeline for audio content moderation. Big question, lots of moving parts, and I definitely underestimated how deep they wanted to go on the operational side.

Questions Asked (1)

Design a human-in-the-loop review subsystem for audio content moderation. Walk through how detections become review tasks, how you triage them into queues by severity, what the reviewer UI needs, SLAs, sampling and consensus mechanisms for quality, gold-standard audits, escalation and requeue logic, access control for sensitive audio, audit logging, how reviewer feedback feeds back into model thresholds and training data, and how you'd plan reviewer capacity and control backlog.

System DesignTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This was one question but it basically had twelve sub-questions baked in.

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AI HintsAI Generated

Suggested Approach

Structure your answer around the end-to-end pipeline: detection → task creation → triage → review → feedback. Emphasize trade-offs between automation and human review, and how you'd measure and optimize the system. Show you understand both technical and operational aspects, including SLAs, quality control, and capacity planning.

Pro tip: Tie everything back to business impact: how your design reduces harmful content exposure, improves reviewer efficiency, and scales with Roblox's massive audio volume. Mention specific metrics like precision/recall, review throughput, and backlog burn rate.

1. Detection to Task Creation

Explain how model detections (with confidence scores) become review tasks, including deduplication, batching, and priority assignment based on severity and confidence.

2. Triage and Queue Management

Describe how tasks are routed to queues by severity (e.g., high, medium, low) and how you'd handle overflow, reassignment, and dynamic prioritization.

3. Reviewer UI and Workflow

Outline key UI features: audio playback with waveform, context (user history, metadata), decision options (approve, reject, escalate), and keyboard shortcuts for efficiency.

4. Quality Control and Feedback Loops

Cover sampling, consensus, gold-standard audits, and how reviewer decisions feed back into model thresholds and training data.

5. Operational Excellence

Discuss SLAs, access control, audit logging, escalation/requeue logic, and capacity planning to control backlog.

Key Points to Mention

Severity-based triage with SLAs (e.g., high severity reviewed within 1 hour)
Consensus mechanisms: multiple reviewers for ambiguous cases, gold-standard audits for quality
Feedback loop: use reviewer labels to retrain models and adjust thresholds dynamically
Access control: role-based permissions, encryption, and audit logs for sensitive audio
Capacity planning: forecast volume, monitor backlog, and scale reviewer pool with surge support
Metrics: track precision/recall, review throughput, backlog age, and reviewer agreement

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.