In April 2025, Zejun Huang developed support for the permute multi-embedding function in torch export for LPV embeddings within the ROCm/FBGEMM repository. He implemented graph mode lowering registration and introduced an FP16 reference kernel to accelerate LPV embedding processing, enabling models to leverage improved inference throughput. His work focused on enhancing embedding workload support, allowing LPV models to utilize more efficient embedding operations. Zejun used C++ and PyTorch to deliver this feature, demonstrating expertise in deep learning and embedding layers. The solution addressed the need for faster, more flexible embedding processing, contributing measurable business value through technical depth and precision.