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

Senior

Jun 2026

Summary

System design round at Anthropic for a software engineer role, focused entirely on building an online model inference serving system. The scope was pretty broad and covered everything from request routing to GPU memory management, so it felt more like a senior-level architecture conversation than a typical design question.

Questions Asked (1)

Design an online model inference serving system. Walk through functional and non-functional requirements, the full request path, inference worker internals, reliability mechanisms, and observability.

System DesignTechnical Trade-offsA/B Testing & Experimentation

Author's notes

This one sprawled in a way I didn't anticipate.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then walk through the end-to-end request path from client to inference worker, covering reliability and observability at each stage. Emphasize trade-offs between latency, throughput, cost, and reliability, and how you would measure and iterate on them.

Pro tip: Anchor your design around SLOs (e.g., p99 latency, availability) and explicitly discuss how you'd handle failures and degradation, since production ML serving is as much about reliability as it is about model performance.

1. Clarify requirements and scope

Ask about expected QPS, latency SLOs, model size, hardware constraints, and whether the system supports multiple models or A/B testing. Define functional (e.g., accept inference requests, return predictions) and non-functional (scalability, availability, cost) requirements.

2. Design the request path

Outline the flow: client -> API gateway (auth, rate limiting) -> load balancer -> inference service -> model worker -> response. Discuss batching, caching, and routing strategies (e.g., model version, canary).

3. Detail inference worker internals

Explain how a worker loads a model, manages memory (e.g., GPU), performs pre/post-processing, and executes inference. Cover optimizations like dynamic batching, quantization, and model warm-up.

4. Incorporate reliability mechanisms

Describe health checks, retries with backoff, circuit breakers, graceful degradation (e.g., fallback to smaller model), and multi-AZ deployment. Discuss how to handle worker failures and model updates without downtime.

5. Define observability and experimentation

Cover metrics (latency, error rate, throughput, GPU utilization), logging, tracing, and alerting. Explain how to support A/B testing and gradual rollouts with shadow traffic and canary deployments.

Key Points to Mention

Latency vs. throughput trade-offs and how batching affects them
Autoscaling strategies based on queue depth or GPU utilization
Model versioning and safe rollout (canary, shadow, A/B testing)
Caching strategies for repeated queries or embeddings
Failure handling: retries, timeouts, circuit breakers, fallbacks
Observability: metrics, tracing, logging, and SLO monitoring

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