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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Anthropic for a software engineering role. The whole interview was basically one deep problem about duplicate file detection, and they kept pulling on threads until I ran out of answers.

Questions Asked (1)

Design a system to detect and optionally remove duplicate files in a directory tree that may not fit in memory. Cover your hashing strategy, how you handle very large files, memory and I/O constraints, collision handling, and how you'd make it incremental and resumable.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with the obvious: group by file size first to avoid hashing everything, then do a partial hash on the first chunk, then full hash only for collisions.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, file types, deletion safety) and then propose a multi-stage pipeline: metadata pre-filtering, partial hashing, and full hashing only for candidates. Emphasize streaming I/O, bounded memory, and a persistent state store for incremental resumability.

Pro tip: Mention that you'd never delete files without a dry-run and a manifest, and that you'd use a Merkle tree or content-defined chunking to handle large files efficiently while enabling incremental updates.

1. Clarify requirements and constraints

Ask about scale (number of files, total size), file types, whether deletion is required, and memory/IO limits. Establish safety requirements like dry-run and logging.

2. Design a multi-stage detection pipeline

Use file size and other metadata to group candidates, then compute partial hashes (e.g., first/last 4KB) to further narrow, and finally full hashes only for remaining candidates. This minimizes I/O and memory.

3. Handle large files with streaming and chunking

For very large files, stream hashing in fixed-size chunks (e.g., 1MB) to keep memory bounded. Consider content-defined chunking (e.g., Rabin fingerprinting) for incremental hashing and deduplication across similar files.

4. Address memory, I/O, and collision handling

Use external sorting or a database (e.g., SQLite) to store hashes if they don't fit in memory. Choose a strong hash (e.g., SHA-256) to make collisions negligible; if using a faster hash, verify with byte-by-byte comparison.

5. Make it incremental and resumable

Persist state (file paths, sizes, mtimes, hashes, and progress) in a database or log. On restart, skip already processed files and resume from the last checkpoint. Use file mtime and size to detect changes.

Key Points to Mention

Use a tiered hashing strategy: size → partial hash → full hash to reduce I/O.
Stream file reads in chunks to keep memory usage constant, regardless of file size.
Choose a cryptographically strong hash (e.g., SHA-256) to avoid collisions, or verify with byte comparison if using a faster hash.
Store intermediate results in an external database (e.g., SQLite) or sorted files to handle datasets larger than memory.
Implement resumability by persisting progress and using file metadata (size, mtime) to skip unchanged files.
Ensure safety with dry-run mode, logging, and optional quarantine instead of immediate deletion.

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