Developed and integrated a CUDA-based timestep embedding kernel for the kvcache-ai/sglang repository, focusing on accelerating temporal data processing within deep learning pipelines. The work involved implementing new embedding classes and integration hooks, allowing seamless connection with existing model architectures and embedding management systems. Utilizing CUDA and Python, the solution improved scalability and performance for time-series workloads by optimizing the diffusion pathway and enabling faster embedding operations. This feature laid the foundation for broader adoption of efficient temporal embeddings in downstream machine learning models, demonstrating depth in CUDA programming and PyTorch while addressing the need for high-performance, scalable time-based data processing.