Worked extensively on PyTorch and related repositories, focusing on GPU performance optimization, CI stability, and test coverage for ROCm backends. Developed a partitioned buffer approach for scatter add in pytorch/pytorch, reducing atomic contention and improving scalability for large models using Python and CUDA. Addressed CI flakiness by fixing submodule cloning in graphcore/pytorch-fork and expanded ROCm test coverage, enabling more reliable releases. Enhanced benchmark accuracy and reliability by correcting expected results and aligning with external references. Demonstrated strong debugging, backend development, and benchmarking skills, consistently improving performance, correctness, and developer productivity across C++, Python, and GPU programming domains.