← Anthropic Interview Insights
Not a lot I can add here since the original post was pretty sparse.
Start by clarifying requirements: scale (requests per second, data volume), latency expectations, model size, and hardware constraints. Then propose a high-level architecture with a focus on batching strategies, resource management, and trade-offs between throughput and latency. Finally, dive into key components like request queuing, dynamic batching, and monitoring.
Pro tip: Emphasize the importance of measuring and optimizing for the actual workload—many candidates jump to complex solutions without understanding the data distribution and access patterns. Also, discuss how you would handle failures and ensure idempotency in a distributed batch system.
Ask questions to understand the scale, latency requirements, model characteristics, and available hardware. This ensures your design is tailored to the problem.
Outline the main components: a request queue, a batching service, inference workers, and a results store. Explain how requests flow through the system.
Discuss how to group requests into batches—static vs. dynamic batching, batch size selection, and trade-offs between throughput and latency.
Explain how to allocate resources (GPU/CPU), autoscale workers based on load, and handle failures and retries.
Describe metrics to track (throughput, latency, error rates) and how to use them to optimize batch size and resource utilization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.