Inference Systems Performance Architect

You will be the architect for end-to-end inference performance: how a request moves through tokenization, prefill, decode, and the fabric between them, and how a deployment is sized against customer SLOs. The work has two coupled pillars. One is reproducible workload capture and benchmarking, building replayable representations of real and increasingly agentic traffic so measurements reflect production rather than a naive load script. The other is performance modeling and simulation, turning measurement into a what-if capability for configurations and hardware that do not exist yet.

The frontier you will help define is heterogeneous, disaggregated inference, with GPU on prefill and the RDU on decode. That opens hard problems in networking, storage, prompt caching, and tail-latency-bound data movement. Inference systems performance is a young field and most answers are still being discovered, so you will spend your time on ambiguous problems with no established solution, and you will set the technical direction that others build on.