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

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Jul 2026

Summary

Anthropic system design round, got a routing problem that looked deceptively simple at first glance but kept expanding. The follow-up questions about consistency and load were where things got interesting.

Questions Asked (1)

You have multiple GPT servers and a stream of incoming prompts. Design a routing system using a hash-table-based approach so that the same prompt always goes to the same server. Implement add_server, remove_server, and route(prompt), and walk through your data structure choices. Then extend it to handle load balancing and what happens when servers are added or removed.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the obvious stuff, md5 the prompt, mod by server count, done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: consistency (same prompt → same server), load balancing, and dynamic server membership. Then design a hash table mapping prompt hashes to server IDs, using consistent hashing with virtual nodes to minimize remapping when servers change. Implement add_server, remove_server, and route, and discuss trade-offs like load distribution and failure handling.

Pro tip: Mention that consistent hashing is used by real systems like DynamoDB and Cassandra, and that virtual nodes help balance load. Also, note that you'd need to handle server failures gracefully, perhaps with replication or fallback.

1. Clarify Requirements and Constraints

Ask about consistency guarantees, load balancing goals, server capacity, and whether prompts can be hashed deterministically. Confirm that the system should be dynamic (servers added/removed).

2. Design Core Data Structure

Propose a hash table mapping prompt hashes to server IDs. For dynamic membership, use consistent hashing with a ring and virtual nodes to distribute load evenly and minimize remapping.

3. Implement Operations

Define add_server (insert virtual nodes into ring), remove_server (remove nodes and reassign affected prompts), and route(prompt) (hash prompt, find next server on ring). Discuss time complexity.

4. Extend for Load Balancing and Dynamic Changes

Explain how virtual nodes improve balance. Discuss strategies for handling hotspots (e.g., rehashing, bounded loads) and the impact of adding/removing servers (only a fraction of prompts remapped).

5. Address Trade-offs and Failure Handling

Compare consistent hashing with alternatives (e.g., modulo hashing). Discuss replication, health checks, and fallback routing for server failures.

Key Points to Mention

Consistent hashing with virtual nodes for even load distribution and minimal remapping.
Hash function choice (e.g., MD5, SHA-1) and collision handling.
Time complexity: O(log N) for routing with a sorted ring, O(1) for hash table lookup.
Trade-offs: consistent hashing vs. modulo hashing (remapping cost), virtual node count tuning.
Handling server failures: replication, health checks, and fallback strategies.
Load balancing metrics: monitor server load and adjust virtual nodes or use bounded loads.

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