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

SeniorPrefer not to say

Jul 2026

Summary

System design interview at Anthropic for a software engineer role. The question was a single massive open-ended prompt covering basically every dimension of backend architecture you can think of. Felt like they were stress-testing how far you could go rather than whether you'd hit a specific answer.

Questions Asked (1)

Design a scalable backend service that handles high-volume network I/O for millions of users, covering requirements, APIs, data models, capacity estimates, high-level architecture, scaling strategies, security, failure handling, observability, and edge cases like partial uploads or slow clients.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This was the whole interview, basically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then walk through a structured design covering APIs, data models, capacity estimates, and high-level architecture. Emphasize scalability, fault tolerance, and observability, and discuss trade-offs for handling edge cases like partial uploads and slow clients.

Pro tip: Anchor your design around a concrete use case (e.g., file uploads or real-time messaging) to make abstract concepts tangible, and proactively discuss trade-offs (e.g., consistency vs. availability) to demonstrate senior-level thinking.

1. Clarify Requirements and Constraints

Ask questions to understand functional and non-functional requirements, such as expected traffic, data size, latency, consistency, and budget. Define the scope and prioritize features.

2. Define APIs and Data Models

Outline the key API endpoints (e.g., upload, download, status) and the data entities (e.g., user, file, session) with their relationships. Consider idempotency and versioning.

3. Estimate Capacity and Design High-Level Architecture

Calculate QPS, storage, and bandwidth needs. Sketch a diagram with components like load balancers, API gateways, stateless services, databases, caches, and message queues.

4. Address Scaling, Security, and Failure Handling

Discuss horizontal scaling, sharding, replication, and CDNs. Cover authentication, encryption, rate limiting, and DDoS protection. Explain retries, circuit breakers, and graceful degradation.

5. Cover Observability and Edge Cases

Describe monitoring, logging, and tracing. Detail handling of partial uploads (e.g., resumable uploads), slow clients (e.g., timeouts, backpressure), and other edge cases like network partitions.

Key Points to Mention

Use of asynchronous I/O and event-driven architecture (e.g., epoll, io_uring) for high concurrency.
Resumable uploads via chunked transfers and unique upload IDs to handle partial uploads.
Backpressure mechanisms and timeouts to manage slow clients without resource exhaustion.
Horizontal scaling with stateless services, sharding, and consistent hashing.
Security measures: TLS, OAuth, rate limiting, and input validation.
Observability: metrics (e.g., Prometheus), logging (e.g., ELK), and tracing (e.g., Jaeger).

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