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

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Jul 2026

Summary

System design round at Anthropic for a software engineer role. The whole session was basically one big rate limiter question that kept getting harder as it went on, from algorithm selection all the way into distributed systems territory.

Questions Asked (2)

Design a rate limiter that allows at most N requests per second per user. The required API is allow(user_id, now_ts) returning a boolean. Walk through your algorithm options and justify your choice.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

They wanted me to actually compare the algorithms, not just pick one and run.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (e.g., strictness, distributed vs. single-node, memory constraints) and then compare algorithms like fixed window, sliding window log, sliding window counter, and token bucket. Choose one based on trade-offs and walk through the implementation details, including data structures and concurrency considerations.

Pro tip: Demonstrate awareness of the distributed setting by discussing how to shard by user_id and handle clock skew, and mention that the choice often depends on whether you need strict enforcement or can tolerate bursts.

1. Clarify requirements and constraints

Ask about expected scale, whether the limiter is distributed, tolerance for bursts, and memory constraints. This ensures your solution aligns with the actual needs.

2. Outline algorithm options

Briefly describe fixed window, sliding window log, sliding window counter, and token bucket, highlighting their pros and cons in terms of accuracy, memory, and burst handling.

3. Select and justify an algorithm

Choose one algorithm (e.g., sliding window counter or token bucket) and explain why it fits the requirements, referencing trade-offs like memory usage, precision, and simplicity.

4. Detail the implementation

Describe the data structures (e.g., hash map of user_id to counter/timestamp), the allow function logic, and how to handle concurrency and distributed coordination (e.g., Redis, sharding).

5. Discuss edge cases and optimizations

Cover clock skew, cleanup of stale entries, and potential optimizations like using a sliding window counter to reduce memory while maintaining accuracy.

Key Points to Mention

Fixed window vs. sliding window vs. token bucket: trade-offs in accuracy, memory, and burst tolerance
Data structures: hash map with per-user state (e.g., timestamps, counters) and efficient cleanup
Distributed considerations: sharding by user_id, using Redis or similar for shared state, handling clock skew
Concurrency: thread safety and atomic operations in a single-node or distributed setup
Memory management: eviction policies for inactive users and bounding memory usage
Strictness: whether to allow bursts (token bucket) or enforce hard limits (sliding window log)

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

Now extend your rate limiter to a distributed setting. Where do you store state, how do you handle clock skew across nodes, and how do you decide whether to fail open or closed when the store is unavailable?

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is where it got interesting.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: scale, latency, consistency needs, and failure tolerance. Then propose a distributed architecture using a centralized store like Redis with atomic operations, discuss clock skew mitigation via logical clocks or server-side timestamps, and outline a fail-open/fail-closed decision framework based on criticality and blast radius.

Pro tip: Emphasize that rate limiting is a trade-off between accuracy and availability; propose a hybrid approach where critical endpoints fail closed while non-critical ones fail open, and mention the use of token buckets with local caching to reduce store dependency.

1. Clarify Requirements and Constraints

Ask about scale (requests per second, number of nodes), latency requirements, consistency needs (strict vs eventual), and the cost of false positives/negatives. This shapes the entire design.

2. Choose a Distributed State Store

Propose a centralized store like Redis or a distributed cache (e.g., Memcached) with atomic operations (INCR, EXPIRE) for rate limiting. Discuss sharding by user/IP for scalability and replication for availability.

3. Handle Clock Skew and Synchronization

Avoid relying on local clocks; use server-side timestamps from the store or logical clocks (e.g., Lamport timestamps). For sliding windows, use store-based time or a centralized time service.

4. Decide Fail-Open vs Fail-Closed Policy

Define a policy based on endpoint criticality: fail closed for security-sensitive operations (e.g., login), fail open for non-critical (e.g., read-only APIs). Implement circuit breakers and fallback to local rate limiting.

5. Address Trade-offs and Monitoring

Discuss trade-offs: accuracy vs availability, latency vs consistency. Propose monitoring for store health, rate limit hits, and fallback activation to detect issues.

Key Points to Mention

Use of Redis with atomic operations (INCR, EXPIRE) for distributed rate limiting.
Sharding by user ID or IP to distribute load and avoid hot spots.
Clock skew mitigation: server-side timestamps or logical clocks instead of local node clocks.
Fail-open vs fail-closed decision based on endpoint criticality and business impact.
Fallback mechanisms: local rate limiting or cached counters when store is unavailable.
Monitoring and alerting for store availability and rate limit effectiveness.

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