Anthropic·Software Engineer·Online Assessment (OA)·Senior

Senior

Jul 2026

Summary

Anthropic AI Safety Fellow interview had an OA-style system design problem that was way more infrastructure-heavy than I expected for a safety-focused role. The whole thing revolved around consistent hashing for GPU request routing, which felt more like a distributed systems eng round than anything safety-adjacent.

Questions Asked (1)

Design a request-routing layer using consistent hashing to distribute LLM inference requests across a pool of GPU servers, with the goal of maximizing KV-cache reuse and minimizing tail latency. Your solution should include a hash ring with virtual nodes, a routing function that uses session or prompt prefix to select a server, and correct behavior when servers are added or removed. Also discuss why prompt-prefix locality matters here and what trade-offs come with this approach.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

I knew consistent hashing from a distributed systems course but had never thought about it in the context of KV-cache locality before.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a consistent hashing ring with virtual nodes to map session/prompt prefixes to GPU servers, ensuring minimal disruption on scaling. Explain how this maximizes KV-cache reuse and reduces tail latency, and discuss trade-offs like load imbalance and cache eviction.

Pro tip: Emphasize that consistent hashing alone isn't enough—you need to consider cache-aware routing and fallback strategies for when a server is overloaded or fails, and quantify the impact on tail latency.

1. Clarify Requirements and Constraints

Ask about scale (number of servers, request rate), latency SLOs, cache size, and failure handling. Confirm that the goal is to maximize KV-cache reuse while minimizing tail latency.

2. Design the Hash Ring with Virtual Nodes

Describe a consistent hash ring where each physical server is represented by multiple virtual nodes (e.g., 100-200) to ensure even load distribution. Hash function should map both servers and request keys (session ID or prompt prefix) to the ring.

3. Define the Routing Function

Explain that for each request, compute the hash of the session ID or prompt prefix, then walk clockwise on the ring to find the first server. This ensures requests with the same prefix go to the same server, maximizing KV-cache hits.

4. Handle Server Addition/Removal

When a server is added or removed, only a fraction of keys are remapped (on average, K/N keys where K is total keys and N is number of servers). This minimizes cache misses and disruption. Mention that virtual nodes help with smooth rebalancing.

5. Discuss Trade-offs and Mitigations

Address trade-offs: potential load imbalance due to hot prefixes, cache eviction when servers change, and the need for fallback routing if a server is overloaded. Suggest mitigations like bounded loads, cache-aware routing, and replication of hot prefixes.

Key Points to Mention

Consistent hashing with virtual nodes ensures even distribution and minimal remapping on scaling.
Prompt-prefix locality: requests with the same prefix share KV-cache, reducing computation and latency.
Tail latency reduction: cache hits avoid recomputation, leading to more predictable response times.
Trade-offs: load imbalance from hot prefixes, cache eviction on server changes, and potential for increased latency if routing to a distant server.
Mitigations: bounded-load consistent hashing, cache-aware routing, and fallback to other servers when overloaded.
Monitoring and metrics: track cache hit rate, tail latency, and load distribution to validate the design.

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