Worked on performance optimization within the apple/axlearn repository, focusing on enhancing the SpmdTrainer’s configuration handling. Addressed evaluation overhead by eliminating unnecessary deep-copying of configuration objects, which improved throughput and resource efficiency during model evaluation. The approach emphasized reliability and maintainability, with a clear and traceable commit history. Utilized Python programming and applied machine learning knowledge to ensure the solution integrated smoothly with existing workflows. No major bugs were reported during this period, reflecting a focus on proactive performance improvements rather than reactive bug fixing. The work demonstrated depth in performance optimization and careful attention to engineering best practices.