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

Senior
Jun 2026

Summary

Interviewed at Anthropic for a SWE role and got a system design question about building a file cache. Not much else to report, pretty focused session.

Questions Asked (1)

Q1

Design a file cache system.

System DesignTechnical Trade-offs
Author's notes

Started with the basics, eviction policies, LRU vs LFU, and then got into storage layers pretty quickly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level design with core components like cache eviction, storage, and concurrency. Discuss trade-offs between different strategies (e.g., LRU vs. LFU, memory vs. disk) and how they impact performance, scalability, and reliability.

Pro tip: Emphasize the importance of measuring cache effectiveness with metrics like hit ratio and latency, and be prepared to discuss how you would handle cache invalidation and consistency in a distributed environment.

1. Clarify Requirements

Ask questions to understand the scope: file sizes, access patterns, consistency needs, scalability, and performance targets. This ensures your design addresses the actual problem.

2. High-Level Design

Outline the main components: cache storage (memory/disk), eviction policy, indexing, and concurrency control. Sketch a simple architecture diagram.

3. Deep Dive into Key Components

Discuss eviction policies (LRU, LFU, etc.), data structures (hash maps, linked lists), and how to handle concurrent access. Consider trade-offs for each choice.

4. Address Scalability and Reliability

Explain how the design scales (sharding, replication) and handles failures (persistence, recovery). Discuss consistency models if distributed.

5. Evaluate Trade-offs and Metrics

Summarize trade-offs (e.g., memory vs. disk, latency vs. throughput) and propose metrics to monitor cache performance (hit ratio, eviction rate).

Key Points to Mention

  • Eviction policies (LRU, LFU, FIFO) and their impact on hit ratio
  • Concurrency control (locking, lock-free data structures) for thread safety
  • Storage options (in-memory, SSD, HDD) and tiered caching
  • Cache invalidation strategies (TTL, write-through, write-back)
  • Scalability considerations (sharding, consistent hashing, replication)
  • Monitoring and metrics (hit ratio, latency, throughput)

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