Worked on the pytorch/executorch repository to address a critical issue in quantized model inference by fixing the quantization annotation for 16-bit LayerNorm. Applied expertise in machine learning and quantization to ensure that quantized inference remained stable and compatible with external toolchains, particularly aligning with Qualcomm AI Engine Direct integration. Developed and integrated comprehensive unit tests in Python to cover the 16a4w LayerNorm path, effectively preventing future regressions. This work improved deployment readiness and reliability for quantized models within the repository, demonstrating a methodical approach to bug fixing and test-driven development in a complex machine learning codebase.