Principal Architect, AI Inference Networking - NIXL and Dynamo

Responsibilities

• Drive NIXL adoption in China, from the first architecture conversation through prototype and benchmark to production rollout and upstream contribution.
• Profile customer inference stacks and fix what's costing throughput and TTFT: KV cache movement, memory registration, transfer scheduling.
• Write NIXL backends and plugins, extend Dynamo integrations, and contribute optimizations upstream to NIXL and GPUDirect class data paths.
• Help define the APIs that make the data path dynamic, supporting elastic scaling, worker churn and runtime rerouting in production inference clusters.
• Bring the region's requirements into NVIDIA's roadmap with enough technical authority to change it.
• Grow the local community around NIXL through talks, benchmarks and reference architectures.
• What we need to see
• M.Sc. or Ph.D. in CS/EE/CE, or equivalent depth earned in industry.
• 12+ years building or optimizing large scale distributed systems: AI inference or training infrastructure, communication libraries, high performance networking, or HPC runtimes.
• Strong systems programming in C++ and Python, with performance critical code shipped.

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