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

d...