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# Research Engineer - [Seed Model - Infra - Training Performance & ML Compilation (Torch Compile)]

- **Company:** BYTEDANCE INC.
- **Location:** Seattle, WA, United States
- **Salary:** $241,680.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Nvidia CUDA, Data Structures, Software Debugging, Distributed Computing Environment, Python (Programming Language), Linux Kernel, Open Source Technology, Performance Tuning, System Programming, Reinforcement Learning, Pytorch, Deep Learning, Bytecode, Information Technology
- **Published:** September 3, 2026
- **Apply:** https://www.gamesjobsdirect.com/job/bytedance/research-engineer-seed-model-infra-training-performance-ml-compilation-torch-compile/357557

## About the Role

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).

## Description

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 PyTorch 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.