Worked on optimizing memory management for multi-context workloads in the pytorch/executorch repository by tuning the Qualcomm AI Engine’s spill fill buffer size. Focused on refining the maximum buffer setting to improve resource utilization and throughput during concurrent model execution, this effort targeted scalable inference scenarios in deep learning. The approach involved C++ development and Python scripting to adjust and validate buffer sizing, ensuring stability and efficiency under representative workloads. By aligning the optimization with performance goals, the work enhanced both memory management and operational stability, supporting more reliable and efficient AI inference across diverse deployment contexts without introducing new bugs.