Our ML accelerator platform spans custom silicon, supercomputing software, compiler stacks, runtime libraries, and distributed inference environments. Performance at this scale is no longer a device-level question — it is a high-performance distributed system problem. You will define the performance metrics that connect raw hardware signals to distributed workload context, ML cluster dynamics, pod communication patterns, and emergent bottlenecks.
This role requires more than telemetry. You will establish new abstractions, structured counter ontologies, cross-layer event correlation frameworks, distributed time-alignment strategies, and scalable reasoning systems operating across nodes, racks, and clusters. Working at the intersection of hardware design, driver architecture, runtime systems, and ML infrastructure, you will shape how these layers expose and consume performance intelligence. This is a foundational role defining not just tooling, but how our platform reasons about efficiency, scalability, and system behavior for years to come.