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

Senior
May 2026

Summary

System design round at OpenAI for a software engineering role. The whole session was basically one big deep-dive into video storage infrastructure, covering everything from upload to CDN delivery. Dense and exhausting.

Questions Asked (1)

Q1

Design the storage system for a large-scale video publishing platform like YouTube, covering the full lifecycle from upload to delivery.

System DesignTechnical Trade-offsData Modeling
Author's notes

This was the whole interview basically.

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Suggested Approach

Start by clarifying requirements and scale (e.g., daily uploads, video sizes, global audience) to set the stage. Then walk through the full lifecycle: upload, processing, storage tiers, metadata, and delivery, highlighting trade-offs and optimizations at each stage. Conclude with a summary of key decisions and how they meet the requirements.

Pro tip: Emphasize the importance of a content delivery network (CDN) and adaptive bitrate streaming for global low-latency delivery, and discuss how to handle hot vs. cold storage to optimize cost and performance.

1. Clarify Requirements and Scale

Ask about expected upload volume, video sizes, geographic distribution, latency requirements, and budget constraints to tailor the design.

2. Design Upload and Ingestion Pipeline

Outline how videos are uploaded (e.g., resumable uploads, chunking), validated, and queued for processing, ensuring reliability and scalability.

3. Plan Video Processing and Transcoding

Describe transcoding into multiple formats/resolutions, generating thumbnails, and extracting metadata, using distributed workers for parallelism.

4. Choose Storage Architecture and Data Model

Select storage solutions for raw videos, transcoded outputs, and metadata (e.g., object storage, CDN, databases), and define schemas for efficient retrieval.

5. Design Delivery and Global Distribution

Explain how videos are served via CDN with adaptive bitrate streaming, caching strategies, and how to handle popular vs. long-tail content.

Key Points to Mention

  • Use of object storage (e.g., S3) for durability and scalability, with lifecycle policies to move older content to cheaper tiers.
  • Transcoding pipeline with parallel processing and job queues (e.g., Kafka, SQS) to handle large volumes.
  • Metadata storage in a distributed database (e.g., Cassandra, DynamoDB) for fast lookups and scalability.
  • CDN integration for global low-latency delivery, with edge caching and adaptive bitrate streaming (HLS/DASH).
  • Trade-offs between consistency, availability, and cost (e.g., eventual consistency for metadata, strong consistency for uploads).
  • Monitoring and analytics for storage usage, delivery performance, and cost optimization.

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