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

Senior

Apr 2026

Summary

Interviewed for a software engineering role at Anthropic and got a system design question about building an inference API. Pretty focused session, just the one problem as far as I can tell.

Questions Asked (1)

Design an inference API.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This is a meaty one for an AI company and I probably underestimated how deep they'd want to go.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints of the inference API, such as expected latency, throughput, model size, and use cases. Then propose a high-level architecture that covers request handling, model serving, scaling, and monitoring, and dive into trade-offs for key components like batching, caching, and hardware selection.

Pro tip: Emphasize the importance of observability and graceful degradation—showing you think about production readiness beyond just the happy path. Also, relate your design to Anthropic's focus on safety and reliability, e.g., by including rate limiting and input validation.

1. Clarify Requirements

Ask questions to understand the expected scale, latency, throughput, model types, and client needs. This ensures your design is tailored to the actual problem.

2. High-Level Architecture

Outline the main components: API gateway, load balancer, inference service, model repository, and monitoring. Explain how requests flow through the system.

3. Deep Dive into Key Components

Discuss trade-offs for batching, caching, hardware (GPU/CPU), and scaling strategies (horizontal vs. vertical). Consider synchronous vs. asynchronous inference.

4. Address Reliability and Security

Cover rate limiting, authentication, input validation, and fallback mechanisms. Mention monitoring, logging, and alerting for production readiness.

5. Summarize and Evaluate

Recap the design, highlighting how it meets the requirements. Discuss potential bottlenecks and future improvements.

Key Points to Mention

Batching strategies (dynamic batching, micro-batching) to optimize GPU utilization and throughput.
Caching mechanisms (e.g., Redis) for frequent requests to reduce latency and cost.
Autoscaling and load balancing to handle variable traffic, possibly using Kubernetes.
Model versioning and A/B testing to support safe rollouts and experimentation.
Observability: metrics (latency, error rates), logging, and tracing for debugging and performance monitoring.
Security: API keys, rate limiting, and input sanitization to prevent abuse and ensure safety.

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