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

SeniorPrefer not to say

Jun 2026

Summary

System design round at Anthropic for a software engineer role, focused entirely on building an inference API service for a large model. The interviewer pushed hard on implementation specifics, not just high-level architecture, which caught me a bit off guard.

Questions Asked (1)

Design an inference API service that serves a large model (like an LLM or vision model) behind a request/response interface. Walk through API design, request batching, scheduling, autoscaling, model loading, GPU vs CPU trade-offs, monitoring, and SLOs.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

I started with the API surface and worked outward, which felt natural, but I underestimated how deep they wanted to go on the GPU execution side.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (model size, latency/throughput targets, hardware constraints) and then walk through the system end-to-end: API design, batching/scheduling, autoscaling, model loading, hardware trade-offs, monitoring, and SLOs. Emphasize trade-offs and justify decisions based on the specific use case, showing awareness of bottlenecks like GPU memory and network latency.

Pro tip: Anchor your design around SLOs (e.g., p99 latency) and show how each component (batching, autoscaling) directly impacts them; this demonstrates production maturity and customer empathy.

1. Clarify Requirements and Constraints

Ask about model size, expected QPS, latency SLOs, cost budget, and hardware availability. This shapes all subsequent design decisions.

2. Design the API and Request Handling

Define REST/gRPC endpoints, request/response schemas, authentication, and error handling. Consider synchronous vs asynchronous patterns for long-running inferences.

3. Implement Batching and Scheduling

Explain dynamic batching to maximize GPU utilization, with a scheduler that balances latency and throughput. Discuss queue management and prioritization.

4. Address Autoscaling, Model Loading, and Hardware

Describe autoscaling policies based on queue depth or latency, model loading strategies (preload, lazy load, caching), and GPU vs CPU trade-offs (cost, performance, availability).

5. Define Monitoring and SLOs

Outline key metrics (latency, throughput, error rates, GPU utilization) and how to set SLOs, alerting, and capacity planning to meet them.

Key Points to Mention

Dynamic batching with a max batch size and timeout to balance latency and throughput
Autoscaling based on custom metrics like queue length or GPU utilization, with scale-to-zero considerations
Model loading strategies: keeping models warm, using model servers (e.g., Triton, TorchServe), and handling cold starts
GPU vs CPU trade-offs: GPU for large models, CPU for smaller or cost-sensitive workloads; consider mixed deployment
Monitoring: track p50/p90/p99 latency, throughput, error rates, GPU memory/utilisation, and queue depth
SLOs: define latency and availability targets, error budgets, and how they influence design (e.g., batching timeout)

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