← Anthropic Interview Insights
This one took me a minute to even figure out what angle they wanted.
Start by clarifying requirements and constraints (latency SLOs, throughput, cost, model types) and then present a high-level architecture that separates the API layer from the batching scheduler and inference workers. Walk through the request lifecycle, batching strategies (time-based, size-based, continuous batching), and trade-offs around latency vs. throughput, then dive into scaling, fault tolerance, and monitoring.
Pro tip: Emphasize that batching is not just about grouping requests but also about smart scheduling: use priority queues and deadline-aware batching to meet SLAs, and consider prefill/decode separation for LLMs to maximize GPU utilization.
Ask about expected QPS, latency SLOs, model sizes, cost constraints, and whether requests are interactive or offline. This shapes the batching strategy and infrastructure choices.
Define a stateless, scalable API (e.g., REST/gRPC) that accepts sampling requests, validates them, and enqueues them into a distributed queue. Include authentication, rate limiting, and request deduplication.
Implement a scheduler that groups requests based on time windows, batch size limits, and priority/deadlines. Use a distributed queue (e.g., Kafka, Redis) and a batching service that forms batches and dispatches them to inference workers.
Workers pull batches, run them on GPUs with optimized inference (e.g., TensorRT, vLLM), and return results. Consider continuous batching, prefill/decode separation, and dynamic batching to maximize throughput.
Scale horizontally, handle failures with retries and dead-letter queues, and monitor key metrics (latency, batch size, GPU utilization, error rates). Discuss autoscaling and cost optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.