Worked on enhancing model runtime configurability and robustness across multiple repositories, including k2-fsa/sherpa-onnx and ml-explore/mlx-lm. Delivered a dynamic decoder layer configuration feature for the Canary model runtime, enabling adaptable model setups through added metadata. Addressed audio processing stability in Blaizzy/mlx-audio by resolving dependency issues and updated documentation to reflect expanded model support. Improved data integrity and error handling in Python and C++ codebases by preventing input mutation, refining autograd behavior, and strengthening server-side parsing. Focused on backend development, unit testing, and machine learning, the work emphasized reliability, maintainability, and support for evolving deep learning workflows.