Worked on reducing the embedding memory footprint in the pytorch/executorch repository by halving the size of char_codebook and audio_codebook embeddings from 20,000 to 10,000 entries. This optimization targeted only the 10 personas used in production, resulting in approximately 20MB of RAM savings and enabling faster initialization and inference. The approach involved careful memory profiling and embedding management to ensure production alignment without impacting functionality. Implemented as a concise, production-ready code change in Python, the work demonstrated skills in data optimization and machine learning, contributing to more scalable deployments and improved efficiency for production environments. No bugs were addressed.