Hiring Pytorch Automation specialist, with expertise in Pytorch workloads and triton.

Develop PyTorch workloads that stress modellevel execution (e.g., large GEMMs, attention patterns, MoElike behavior, mixed precision, longrunning loops)
Author custom Triton kernels to directly stress hardware execution units, memory hierarchies, and synchronization paths
Build parameterized stress harnesses that can scale with problem size, number of devices, and runtime duration
Integrate workloads with existing tooling for profiling, monitoring, and failure triage
Collaborate with platform, firmware, and SDK teams to ensure workloads target known risk areas and emerging issues
Document usage patterns and provide reproducible scripts for lab and CI usage

Expected Deliverables

A library of reusable PyTorch stress workloads
A set of Tritonbased micro and macrokernels designed specifically for stress and saturation testing
Test harnesses/scripts supporting singledevice and multidevice execution
Documentation describing workload intent, configuration options, and expected stress characteristics

pytorch automation