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

Senior
Jun 2026

Summary

System design round at OpenAI for a backend role, basically one big question about building YouTube from scratch. The scope was massive and I felt like I was constantly playing catch-up across components.

Questions Asked (1)

Q1

Design a large-scale video sharing and streaming platform similar to YouTube, covering upload, transcoding, storage, adaptive bitrate delivery, view counts, comments, recommendations, and search.

System DesignTechnical Trade-offsData Modeling
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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale (e.g., daily uploads, concurrent viewers, global distribution), then outline a high-level architecture covering upload, transcoding, storage, delivery, and metadata services. Dive into 2-3 critical components (e.g., transcoding pipeline, adaptive bitrate streaming, view count consistency) with trade-offs and data models.

Pro tip: Emphasize decoupling and asynchronous processing (e.g., message queues for transcoding, eventual consistency for view counts) to handle scale and failures gracefully. Show awareness of cost and latency trade-offs, especially for storage tiers and CDN caching.

1. Clarify Requirements and Scale

Ask about expected scale (uploads/day, concurrent viewers, storage), functional priorities (e.g., upload latency vs. view count accuracy), and non-functional needs (availability, consistency).

2. High-Level Architecture

Sketch the end-to-end flow: upload service, transcoding pipeline, storage (raw and transcoded), metadata DB, CDN for delivery, and services for views, comments, recommendations, and search.

3. Deep Dive into Critical Components

Pick 2-3 areas to detail, such as transcoding (parallel chunked processing, format selection), adaptive bitrate (HLS/DASH, segmenting), and view count (eventual consistency, sharded counters).

4. Data Modeling and Storage Choices

Discuss data models for videos, comments, and views; choose appropriate stores (e.g., object storage for blobs, NoSQL for metadata, search index for search).

5. Trade-offs and Scaling

Explain trade-offs (e.g., consistency vs. latency for view counts, cost vs. performance for storage tiers) and how to scale each component (sharding, replication, caching).

Key Points to Mention

  • Transcoding pipeline: parallel chunked processing, multiple formats/resolutions, queue-based decoupling for scalability.
  • Adaptive bitrate streaming: HLS/DASH, segmenting videos into small chunks, manifest files, CDN edge caching.
  • View count consistency: eventual consistency, sharded counters, batch updates, and deduplication.
  • Storage strategy: tiered storage (hot/warm/cold), object storage (e.g., S3) for blobs, metadata in distributed DB.
  • Recommendations and search: offline batch processing (e.g., collaborative filtering) + online serving; inverted index for search.
  • Scalability and fault tolerance: sharding, replication, CDN, asynchronous processing, and graceful degradation.

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