Research Engineer — Training Performance & ML Compilation (Torch Compile) - Seed Infra

About the Team:

The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation models.

Responsibilities:

• Optimize training performance for large-scale foundation models through compiler-level techniques, including graph optimization, operator fusion, and kernel generation.
• Develop and extend ML compilation capabilities based on the Py Torch compilation stack (e.g. FX, Dynamo, Inductor) to improve training efficiency across heterogeneous GPU platforms.
• Design and optimize high-performance GPU kernels for training workloads.
• Conduct performance profiling and analysis of large-scale training jobs; identify and resolve bottlenecks in collaboration with research and infrastructure teams.

Requirements:

• 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 Py Torch compilation stack, meeting at least one of the following: direct experience using, debugging, or extending Inductor or FX; proficiency in Triton kernel development; or solid experience with Py Torch computation graph work (graph optimization, graph capture, operator fusion).

• Preferred Qualification(s)

• Experience with Torch Dynamo 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., Py Torch, Triton, Flash Attention).

• Publications in relevant venues (e.g., MLSys, OSDI, ASPLOS).