Minimum Qualification(s)
- Bachelor's degree or above in Computer Science, Electrical Engineering, or a related field.
- Strong proficiency in C/C++ and Python; solid foundations in algorithms, data structures, and systems programming.
- Hands-on experience in training-side performance optimization for deep learning workloads.
- Hands-on experience writing and optimizing GPU kernels (e.g., CUDA, Triton).
- Experience with the PyTorch compilation stack, meeting at least one of the following: Direct experience using, debugging, or extending Inductor or FX.
- Proficiency in Triton kernel development.
- Solid experience with PyTorch computation graph work (graph optimization, graph capture, operator fusion).
Preferred Qualification(s)
- Experience with TorchDynamo or bytecode-level program transformation.
- Experience with Triton compiler internals or other ML compiler backends (e.g., MLIR, LLVM).
- Contributions to related open-source projects (e.g., PyTorch, Triton, FlashAttention).
- Publications in relevant venues (e.g., MLSys, OSDI, ASPLOS).