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 session was basically one deep question about file deduplication that kept branching into new territory every time I thought I'd covered it.

Questions Asked (1)

Design a file deduplication system for a large-scale storage platform. Cover chunking strategies, hash function selection, collision handling, memory constraints for large files, streaming ingestion, parallelization, I/O optimization, and the data structures you'd use for fast lookups.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, storage backend, dedup granularity) and then walk through the pipeline: chunking, hashing, indexing, and storage. Emphasize trade-offs at each stage, especially around memory, I/O, and parallelism, and propose a concrete design with data structures and algorithms.

Pro tip: Anchor your design in real-world systems like content-defined chunking (CDC) with Rabin fingerprints and mention how you'd handle hash collisions with a secondary verification step to avoid data corruption.

1. Clarify Requirements and Constraints

Ask about scale (PB), file sizes, dedup ratio expectations, latency SLAs, and whether dedup is inline or post-process. This shapes chunking and indexing choices.

2. Design Chunking and Hashing

Choose content-defined chunking (e.g., Rabin fingerprint) over fixed-size to handle insertions. Select a strong hash like SHA-256 for collision resistance, and discuss trade-offs with faster hashes like xxHash.

3. Build the Index and Handle Collisions

Use a distributed key-value store (e.g., Cassandra) or a custom hash table with on-disk storage for chunk metadata. For collisions, store full chunk or a second hash to verify.

4. Optimize Memory and I/O

Stream chunks to avoid loading entire files; use buffered I/O and memory-mapped files. For large files, process in windows and use a rolling hash for CDC.

5. Parallelize and Scale

Parallelize chunking and hashing across CPU cores and distribute index lookups across nodes. Use consistent hashing for sharding and batch writes to reduce I/O.

Key Points to Mention

Content-defined chunking (CDC) with Rabin fingerprint for variable-size chunks
Hash function selection: SHA-256 for security vs. xxHash for speed; collision probability and handling
In-memory vs. on-disk index: Bloom filters for fast negative lookups, and LSM trees for write-heavy workloads
Streaming ingestion with bounded memory: process file in chunks, use rolling hash, and avoid full-file buffering
Parallelization: pipeline stages (chunking, hashing, indexing) and sharding by hash prefix
I/O optimization: sequential reads, batched writes, and compression of chunk metadata

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