Sheng Fu contributed to large-scale distributed training systems by enhancing memory management and profiling capabilities across NVIDIA/Megatron-LM and PyTorch repositories. He implemented a persistent buffer fallback in Megatron-LM, using C++ and Python to improve memory efficiency when bucket sizes exceeded allocator capacity. Sheng also expanded PyTorch’s profiling features, enabling collection of tensor shapes and call stacks for deeper performance analysis. In facebookresearch/param, he integrated Lintrunner-based linting to align code quality checks with PyTorch standards, streamlining CI/CD workflows. His work demonstrated depth in distributed systems, performance optimization, and maintainability, addressing both robustness and developer productivity in complex machine learning environments.