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

Senior
Jun 2026

Summary

System design round at OpenAI for a software engineer role, 60 minutes on streaming media. Not a lot of detail to go on but it was a full-length technical session.

Questions Asked (1)

Q1

Design a streaming media system.

System DesignTechnical Trade-offs
Author's notes

60 minutes on this and I still felt like I barely scratched the surface.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level architecture covering ingestion, storage, processing, and delivery. Dive into key components like CDN, transcoding, and adaptive bitrate streaming, discussing trade-offs and scalability.

Pro tip: Emphasize trade-offs between latency, quality, and cost, and how you would monitor and optimize the system using metrics like startup time and rebuffering ratio.

1. Clarify Requirements

Ask about scale (users, concurrent streams), content types (live vs. on-demand), latency requirements, and budget constraints.

2. High-Level Architecture

Sketch the main components: ingestion, transcoding, storage, CDN, and client players. Explain data flow from source to viewer.

3. Deep Dive into Key Components

Discuss transcoding (formats, bitrates), storage (object storage, caching), and delivery (CDN, adaptive bitrate streaming).

4. Address Scalability and Reliability

Explain how to handle spikes, ensure fault tolerance, and use load balancing, replication, and monitoring.

5. Discuss Trade-offs and Optimizations

Compare latency vs. quality, cost vs. performance, and suggest optimizations like edge computing or peer-to-peer.

Key Points to Mention

  • Adaptive bitrate streaming (e.g., HLS, DASH) for varying network conditions
  • CDN for global content distribution and low latency
  • Transcoding pipeline for multiple formats and resolutions
  • Storage solutions: object storage for videos, metadata databases
  • Scalability: horizontal scaling, load balancing, and auto-scaling
  • Monitoring and analytics: QoE metrics like startup time, buffering ratio

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