Worked on enhancing tensor operation capabilities in the graphcore/pytorch-fork repository by delivering MTIA dispatch for the maximum scalar kernel. Focused on backend development using C++ and YAML, the work introduced a new dispatch path for the foreach_tensor_maximum_scalar_kernel_mtia_ function, enabling efficient maximum scalar reductions across tensors. This addition improved throughput and scalability for PyTorch workloads running on Graphcore hardware, while maintaining compatibility with existing MTIA infrastructure. The engineering approach emphasized kernel dispatch design, integration with PyTorch’s backend, and performance optimization, laying groundwork for faster reductions in production machine learning pipelines without introducing regressions or breaking existing workflows.