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Anthropic·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
Apr 2026

Summary

Anthropic software engineer coding round. The core problem was fine but the follow-ups are where things got rough, and I came out feeling like I passed the easy part and stumbled on the parts that actually mattered.

Questions Asked (1)

Q1

Implement a file deduplication system using file size and content hashing.

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

The base problem was pretty straightforward.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, file types, performance needs) and then outline a two-phase approach: first group files by size to quickly eliminate non-duplicates, then compute cryptographic hashes (e.g., SHA-256) only for files with matching sizes to confirm duplicates. Discuss trade-offs between full-file hashing and chunk-based hashing, and consider memory and I/O constraints.

Pro tip: Mention that you can optimize by hashing only the first few kilobytes of files with matching sizes to quickly filter out most non-duplicates, then fall back to full hashing for the remainder—this significantly reduces I/O for large files.

1. Clarify Requirements and Constraints

Ask about scale (number of files, total size), file types, performance requirements, and whether the system is batch or real-time. This shapes the algorithm and data structures.

2. Design Two-Phase Deduplication

Phase 1: Group files by size using a hash map (size -> list of file paths). Phase 2: For each group with more than one file, compute a strong hash (e.g., SHA-256) and group by hash to identify duplicates.

3. Optimize Hashing Strategy

Discuss using partial hashing (e.g., first 4KB) to reduce I/O, then full hashing only for collisions. Consider chunk-based hashing (e.g., Rabin fingerprinting) for large files or incremental deduplication.

4. Handle Edge Cases and Scalability

Address empty files, symbolic links, and concurrent modifications. For scalability, discuss external sorting or distributed processing if the dataset doesn't fit in memory.

5. Analyze Trade-offs and Complexity

Compare time/space complexity of different approaches, and discuss trade-offs between accuracy (hash collisions) and performance. Mention that cryptographic hashes make collisions negligible.

Key Points to Mention

  • Use file size as a fast, cheap first filter to avoid unnecessary hashing.
  • Choose a cryptographic hash like SHA-256 for collision resistance; mention that MD5 is faster but less secure.
  • Optimize I/O by hashing only a portion of the file first, then full file if needed.
  • Consider memory usage: storing all hashes may require external storage or streaming.
  • Discuss scalability: for millions of files, use a database or distributed system like MapReduce.
  • Handle edge cases: empty files, files with same content but different metadata, and concurrent file changes.

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