What this interview will probe
Own the inference and routing layer that decides which model serves each request and runs it efficiently at scale, optimizing throughput, batching, and GPU utilization across Cursor's model fleet. You'll balance quality, latency, and cost in a system serving constant high-volume LLM traffic. A technical interview would probe inference optimization (KV caching, batching, quantization), GPU performance tradeoffs, and how you'd build a routing policy that picks the cheapest model meeting a quality bar.
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