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

Senior

Jun 2026

Summary

System design round at Anthropic for a software engineering role. The prompt was one of those sprawling open-ended ones where they basically hand you a blank canvas and expect you to fill in every layer of the stack, from SLOs down to cost trade-offs and disaster recovery.

Questions Asked (1)

Design a scalable, highly reliable system for a complex open-ended domain (like a content feed, ride matching, or file storage). Walk through functional and non-functional requirements, high-level architecture, core APIs and data models, partitioning and replication strategy, caching across tiers, load balancing and autoscaling, failure handling and disaster recovery, observability and security, capacity planning, and key risks with mitigations.

System DesignTechnical Trade-offsData Modeling

Author's notes

This is basically a 'design everything' prompt and the scope is genuinely intimidating.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the domain and scoping functional and non-functional requirements, then present a high-level architecture that addresses scalability, reliability, and trade-offs. Iterate through each layer (APIs, data models, partitioning, caching, etc.) while justifying decisions and highlighting risks with mitigations.

Pro tip: Demonstrate a bias for simplicity and incremental scaling: start with a simple design that meets requirements, then evolve it as scale increases. Explicitly call out trade-offs (e.g., consistency vs. availability) and tie them back to business needs.

1. Clarify Requirements and Scope

Ask clarifying questions to understand the domain, expected scale, and key priorities (e.g., consistency, latency, cost). Define functional and non-functional requirements, including SLAs/SLOs.

2. High-Level Architecture and Core Components

Sketch a high-level architecture with major components (e.g., load balancers, API gateways, services, data stores, caches). Define core APIs and data models, focusing on entities and relationships.

3. Scalability and Reliability Deep Dive

Detail partitioning/sharding, replication, caching strategies across tiers, load balancing, and autoscaling. Explain how these choices support scalability and reliability.

4. Failure Handling, Observability, and Security

Describe failure detection and recovery, disaster recovery plans, monitoring/alerting, and security measures (authn/authz, encryption, etc.).

5. Capacity Planning and Risk Mitigation

Estimate resource needs, discuss capacity planning, and identify key risks (e.g., hotspots, data loss) with mitigations. Summarize trade-offs and future improvements.

Key Points to Mention

Functional vs. non-functional requirements: clarify read/write patterns, latency, consistency, availability, and durability needs.
Partitioning and replication strategies: consistent hashing, sharding by key, multi-region replication, and trade-offs (e.g., eventual vs. strong consistency).
Caching across tiers: client-side, CDN, application-level, and database caching; cache invalidation strategies and TTLs.
Load balancing and autoscaling: L4 vs. L7 load balancers, health checks, horizontal/vertical scaling, and auto-scaling policies.
Failure handling and disaster recovery: redundancy, failover, backups, multi-region active-active/passive, and RTO/RPO considerations.
Observability and security: metrics, logging, tracing, alerting; encryption in transit/at rest, IAM, and compliance.

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