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

SeniorRejected
May 2026

Summary

Went through a system design round at OpenAI for a software engineer role and got completely blindsided by the topic. No relevant work background, prep had faded, and I left pretty sure I tanked it.

Questions Asked (1)

Q1

Design a video streaming service from scratch.

System DesignTechnical Trade-offs
Author's notes

I had nothing.

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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 video encoding, CDN, and adaptive streaming, discussing trade-offs and scalability at each step.

Pro tip: Emphasize trade-offs and justify your choices based on requirements; interviewers value thoughtful decision-making over listing technologies. Also, proactively mention monitoring, failure handling, and cost considerations to show production maturity.

1. Clarify Requirements

Ask questions to understand scale, features (e.g., live streaming, DRM), latency, and budget constraints. Define functional and non-functional requirements.

2. High-Level Architecture

Sketch the main components: upload/ingest, transcoding pipeline, storage, CDN, and client playback. Explain data flow from upload to viewing.

3. Deep Dive into Key Components

Discuss video encoding (formats, bitrates), adaptive streaming (HLS/DASH), CDN strategy, and storage tiers. Explain how to handle scalability and reliability.

4. Address Trade-offs and Bottlenecks

Analyze trade-offs like cost vs. performance, latency vs. quality, and build vs. buy. Identify potential bottlenecks and mitigation strategies.

5. Wrap Up with Monitoring and Future Work

Mention monitoring, logging, and alerting. Suggest possible improvements like AI-driven recommendations or edge computing.

Key Points to Mention

  • Video encoding and transcoding (codecs, bitrate ladders, parallel processing)
  • Adaptive bitrate streaming protocols (HLS, DASH) and player logic
  • CDN and edge caching for low-latency, scalable delivery
  • Storage solutions (object storage, tiered storage for cost efficiency)
  • Scalability and fault tolerance (microservices, load balancing, replication)
  • Security and DRM (encryption, token-based access, watermarking)

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