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

IntermediatePrefer not to say

Jun 2026

Summary

System design round at Amazon for a software engineer role. The problem felt completely made up, possibly AI-generated, and I had zero prior exposure to anything like it so I just kind of winged it.

Questions Asked (1)

Design a system that handles slow or long-running computations efficiently.

System DesignTechnical Trade-offsAdaptability & Ambiguity

Author's notes

Never seen a problem framed quite like this before.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level architecture that decouples task submission from execution using asynchronous processing and a message queue. Dive into trade-offs between different approaches (e.g., synchronous vs. asynchronous, polling vs. push notifications) and discuss scalability, fault tolerance, and cost.

Pro tip: Emphasize Amazon's leadership principles like Customer Obsession and Ownership by focusing on how your design improves customer experience and takes end-to-end responsibility for the system's reliability and performance.

1. Clarify Requirements

Ask questions to understand the use case: expected latency, throughput, task duration, result delivery method, and failure handling. This shows you avoid assumptions and design for actual needs.

2. High-Level Design

Propose an architecture with components like API gateway, task queue, worker pool, result store, and notification service. Explain how they interact to handle long-running tasks asynchronously.

3. Deep Dive into Components

Discuss specific technologies (e.g., SQS, Lambda, Step Functions, ECS) and how they address scalability, fault tolerance, and cost. Explain how to handle task prioritization, retries, and dead-letter queues.

4. Trade-offs and Alternatives

Compare your approach with alternatives (e.g., synchronous processing, batch processing) and justify your choices based on requirements. Discuss trade-offs like complexity vs. scalability, cost vs. latency.

5. Operational Considerations

Cover monitoring, logging, alerting, and auto-scaling. Discuss how to handle failures, ensure idempotency, and manage backpressure.

Key Points to Mention

Asynchronous processing with message queues (e.g., Amazon SQS) to decouple components and handle spikes.
Worker auto-scaling and task distribution to efficiently utilize resources and reduce cost.
Result storage and retrieval mechanisms (e.g., DynamoDB, S3) with appropriate consistency models.
Notification patterns (e.g., polling, webhooks, WebSockets) for clients to get results.
Fault tolerance: retries, dead-letter queues, idempotent task execution, and exactly-once processing.
Monitoring and observability: CloudWatch metrics, logs, and alarms to track queue depth, worker health, and task success rates.

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