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

Senior
May 2026

Summary

System design round at Amazon for a software engineering role. One question, pretty open-ended, and I definitely underestimated how much ground there was to cover.

Questions Asked (1)

Q1

How would you design Amazon Prime Video?

System DesignTechnical Trade-offsProduct Sense & Ideation
Author's notes

I jumped straight into the video streaming pipeline and spent way too long on CDN and encoding before they nudged me toward user-facing features and scale requirements.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and requirements of Amazon Prime Video as a system design problem, focusing on core functionalities like video streaming, content delivery, and user personalization. Then, outline a high-level architecture covering key components such as CDN, microservices, and storage, and dive into trade-offs for scalability, reliability, and cost. Finally, discuss how to handle peak traffic, global distribution, and data consistency.

Pro tip: Emphasize the importance of adaptive bitrate streaming and global CDN strategy to ensure low-latency, high-quality video delivery, and mention how Amazon's existing infrastructure (e.g., AWS CloudFront) can be leveraged for cost efficiency and scalability.

1. Clarify Requirements and Scope

Ask clarifying questions to understand functional and non-functional requirements, such as supported devices, expected scale, key features (streaming, recommendations, downloads), and constraints like budget or latency.

2. High-Level Architecture

Sketch a high-level design including client apps, API gateway, microservices for user management, content ingestion, recommendation engine, and a CDN for video delivery. Mention data stores like S3 for video storage and DynamoDB for metadata.

3. Deep Dive into Key Components

Elaborate on critical components: video encoding/transcoding pipeline, adaptive bitrate streaming (HLS/DASH), CDN caching strategy, and recommendation system using machine learning. Discuss how to handle user sessions and authentication.

4. Address Scalability, Reliability, and Trade-offs

Discuss scaling strategies (horizontal scaling, sharding, caching), fault tolerance (redundancy, multi-region deployment), and trade-offs between consistency, availability, and cost. Mention monitoring and auto-scaling.

5. Summarize and Discuss Future Improvements

Wrap up by summarizing the design, highlighting how it meets requirements, and suggest potential enhancements like edge computing, AI-driven personalization, or cost optimization.

Key Points to Mention

  • Use of CDN (e.g., Amazon CloudFront) for global video distribution and low-latency streaming
  • Adaptive bitrate streaming protocols (HLS, DASH) to handle varying network conditions
  • Microservices architecture for modularity and independent scaling of services like user auth, recommendations, and billing
  • Data storage solutions: S3 for video content, DynamoDB for metadata, and caching layers (ElastiCache) for performance
  • Recommendation engine leveraging machine learning and user behavior data
  • Multi-region deployment and disaster recovery for high availability

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