Worked on enhancing reliability and configurability across sgl-project/sglang, huggingface/transformers, and fla-org/flash-linear-attention repositories. Focused on robust API development and backend improvements using Python and PyTorch, addressing issues such as invalid input handling, JSON configuration serialization, and CPU stability for model inference. Implemented fixes for gradient correctness in Flash Linear Attention and improved memory management in hybrid caching scenarios. The work emphasized data validation, algorithm optimization, and unit testing to reduce deployment risk and support safer scaling of machine learning services. These contributions improved maintainability and ensured stable, accurate inference for deep learning and NLP applications.