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

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Jun 2026

Summary

System design round at Anthropic for a software engineering role. The whole thing centered on one problem but went pretty deep, covering both implementation details and scaling considerations. Left feeling like I did okay on the basics but could've been sharper on the distributed side.

Questions Asked (1)

Given a directory tree or a stream of files, how would you identify duplicate files (files with identical content)? Walk through a basic implementation and then discuss how you'd scale it.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the obvious: hash file contents, group by hash.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (file size, number of files, memory constraints) and then present a multi-stage approach: first group by file size, then hash file contents to find duplicates. For scaling, discuss distributed hashing, streaming, and trade-offs between exact and approximate methods.

Pro tip: Mention that you can optimize by only hashing files with matching sizes, and for large files, hash in chunks or use a rolling hash to avoid reading entire files. Also, consider using a Bloom filter to quickly eliminate unique files before full hashing.

1. Clarify Requirements and Constraints

Ask about the scale (number of files, total size), memory limits, whether the file set is static or streaming, and if exact duplicates are needed. This shapes the algorithm choice.

2. Basic Implementation: Group by Size then Hash

First, group files by size; files with unique sizes cannot be duplicates. For each size group, compute a cryptographic hash (e.g., SHA-256) of file contents and group by hash. Files with identical hashes are duplicates.

3. Optimize for Large Files and Memory

For large files, compute hash incrementally in chunks to avoid loading entire file into memory. Use a two-level hashing: first a fast non-cryptographic hash (e.g., xxHash) to bucket, then a cryptographic hash for confirmation.

4. Scale to Distributed/Streaming Environment

For distributed systems, partition files by hash prefix across nodes, or use MapReduce: map by size, then by hash. For streaming, maintain a Bloom filter of hashes to quickly identify potential duplicates, then verify.

5. Discuss Trade-offs and Alternatives

Compare exact vs. approximate (e.g., using checksums with collision risk), memory vs. speed, and centralized vs. distributed. Mention that cryptographic hashes have negligible collision probability for practical purposes.

Key Points to Mention

Grouping by file size first drastically reduces the number of files to hash.
Use of cryptographic hash functions (e.g., SHA-256) for content comparison, with negligible collision risk.
Incremental/streaming hashing to handle large files without loading them entirely into memory.
Distributed approaches: consistent hashing, MapReduce, or partitioning by hash prefix.
Bloom filters for efficient pre-filtering in streaming scenarios.
Trade-offs: exact vs. approximate, memory vs. speed, and handling of hash collisions.

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