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

Staff
Apr 2026

Summary

System design round at OpenAI for an infrastructure role. The question was a full-blown video storage platform design, YouTube-scale, and they clearly wanted you to go deep on every layer rather than just sketch boxes on a whiteboard.

Questions Asked (1)

Q1

Design a large-scale video publishing and storage system similar to YouTube, covering the full pipeline from upload through playback at over a billion videos.

System DesignTechnical Trade-offsData Modeling
Author's notes

This one sprawled in every direction fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., 1B+ videos, upload/playback rates, global distribution) before diving into high-level design. Then walk through the end-to-end pipeline: upload, transcoding, storage, metadata, CDN delivery, and playback, making key trade-offs explicit (e.g., consistency vs. availability, cost vs. latency).

Pro tip: Emphasize the importance of a content delivery network (CDN) and adaptive bitrate streaming for playback at scale, and discuss how you'd handle hot videos vs. long-tail content differently to optimize cost and performance.

1. Clarify Requirements and Scale

Ask about expected upload volume, playback QPS, geographic distribution, latency targets, and consistency needs. Establish assumptions like 1B+ videos, 100M daily active users, and global access.

2. High-Level Architecture

Sketch the main components: upload service, transcoding pipeline, object storage, metadata DB, CDN, and playback service. Explain how they interact and the data flow from upload to playback.

3. Deep Dive into Critical Components

Detail the transcoding pipeline (e.g., chunked upload, parallel transcoding, multiple resolutions), storage tiering (hot vs. cold), and metadata sharding. Discuss how to handle failures and retries.

4. Playback and Delivery Optimization

Explain how videos are served via CDN with adaptive bitrate streaming (HLS/DASH), caching strategies, and how to handle viral videos. Discuss pre-fetching and edge caching.

5. Trade-offs and Scalability

Discuss trade-offs: consistency vs. availability for metadata, cost vs. performance for storage tiers, and push vs. pull CDN models. Address scaling bottlenecks and monitoring.

Key Points to Mention

  • Chunked/resumable uploads and parallel transcoding for efficient ingestion
  • Object storage (e.g., S3) with lifecycle policies for hot/cold data
  • Metadata sharding and indexing for fast search and recommendations
  • CDN with adaptive bitrate streaming (HLS/DASH) for global playback
  • Trade-offs: strong vs. eventual consistency, cost vs. latency, push vs. pull CDN
  • Handling viral videos: edge caching, pre-warming, and rate limiting

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