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

SeniorPrefer not to say
May 2026

Summary

Brutal system design round at Anthropic for a software engineer role. One question, but it basically covered every dimension of distributed systems I've ever studied, and then some I hadn't.

Questions Asked (1)

Q1

Design a production-ready file deduplication service. Cover the full architecture including ingestion, chunking, indexing, storage, and metadata layers. Walk through your API design, read and write workflows, and explain how you'd handle consistency, idempotency, fault isolation, failure recovery, and disaster recovery. Also address backfills and compaction/GC, index sharding and rebalancing, deployment and rollback strategies, schema migration, monitoring and SLOs, capacity planning, cost controls, privacy and compliance requirements, and how you'd protect production workloads from degradation.

System DesignTechnical Trade-offsAPI & Integrations
Author's notes

I started with chunking and content-addressable storage because that felt like the core insight, hash the chunks, deduplicate by hash, done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then present a high-level architecture with clear separation of ingestion, chunking, indexing, storage, and metadata. Walk through the write and read paths, emphasizing idempotency, consistency, and fault isolation. Finally, cover operational aspects like backfills, compaction, sharding, deployment, monitoring, and cost controls, tying each to production readiness.

Pro tip: Anchor your design around content-addressable storage and idempotent operations—this simplifies deduplication, consistency, and recovery. Explicitly call out trade-offs (e.g., strong vs. eventual consistency) and justify your choices based on the use case.

1. Clarify Requirements and Scale

Ask about data volume, file sizes, deduplication scope (global vs. per-tenant), latency/throughput SLOs, consistency needs, and compliance constraints. This shapes all subsequent design decisions.

2. High-Level Architecture

Sketch the main components: ingestion service, chunker, index (e.g., hash-based), blob storage, metadata store, and API gateway. Explain how they interact and the data flow.

3. Write and Read Workflows

Detail the write path: client uploads file, service chunks it, computes hashes, checks index for duplicates, stores unique chunks, and updates metadata. For reads: client requests file, service retrieves metadata, fetches chunks, and reassembles. Emphasize idempotency and consistency.

4. Operational Excellence

Cover backfills (e.g., re-chunking existing data), compaction/GC (removing unreferenced chunks), index sharding and rebalancing, deployment/rollback strategies, schema migration, monitoring/SLOs, capacity planning, cost controls, and privacy/compliance.

5. Protecting Production and Failure Recovery

Explain fault isolation (e.g., bulkheads, circuit breakers), failure recovery (retries, idempotent operations, dead-letter queues), disaster recovery (multi-region replication, backups), and how to protect production from degradation (rate limiting, load shedding, canary deployments).

Key Points to Mention

  • Content-addressable storage and chunk-level deduplication using cryptographic hashes (e.g., SHA-256) to ensure data integrity and idempotency.
  • Idempotent write APIs with client-provided request IDs and deduplication at the metadata layer to handle retries safely.
  • Consistency models: strong consistency for metadata (e.g., using a transactional database) and eventual consistency for blob storage, with read-after-write guarantees where needed.
  • Index sharding strategies (e.g., consistent hashing) and rebalancing to handle growth and hotspots, plus compaction/GC to reclaim space from unreferenced chunks.
  • Deployment strategies: blue-green or canary deployments, feature flags, and rollback plans; schema migrations using expand-contract pattern.
  • Monitoring and SLOs: track deduplication ratio, latency, error rates, and storage growth; set alerts and capacity plans; implement cost controls like tiered storage and lifecycle policies.

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