Optimizing performance and scalability of embedding layers involves efficient data loading, dimensionality management, and mixed precision training. Utilizing PyTorch's DataLoader, experimenting with embedding sizes, and implementing techniques like hierarchical softmax can enhance training speed and reduce computational costs. Distributed training further improves scalability for large datasets.