Opportunity Description
Hiring Pytorch Automation specialist, with expertise in Pytorch workloads and triton. Develop PyTorch workloads that stress model‑level execution (e.g., large GEMMs, attention patterns, MoE‑like behavior, mixed precision, long‑running 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 Triton‑based micro‑ and macro‑kernels designed specifically for stress and saturation testing Test harnesses/scripts supporting single‑device and multi‑device execution...