You'll write technical deep dives, build demos, create tutorials, contribute to docs and examples, host workshops, and help developers understand topics like KV cache, continuous batching, prefix caching, prefill and decode, quantization, GPU serving, latency versus throughput, and model-server tradeoffs across vLLM and adjacent systems. We're looking for a inference systems educator-builder: someone who can understand vLLM as a deep LLM inference systems project, teach hard technical concepts clearly, and create public artifacts that help practitioners build better systems.