Worked on the vllm-project/tpu-inference repository to improve hybrid model inference by aligning TPU attention block sizing with backend-driven configuration. Addressed a bug where the attention page size was hardcoded, replacing it so that block size is now determined dynamically by backend settings. This change reduces the risk of misconfiguration and supports safer backend experimentation. Developed and integrated parameterized tests using Python to ensure block size updates are correctly applied across hybrid configurations. Focused on backend development, TPU programming, and robust testing practices, the work enhanced end-to-end correctness for TPU inference and improved maintainability of configuration-driven model deployments.