Worked on the vllm-project/tpu-inference and jeejeelee/vllm repositories to deliver three new features focused on TPU optimization and model deployment. Automated the merging of multi-host JAX profiling data, streamlining profiling workflows and improving analysis accuracy for large-scale machine learning tasks. Enhanced Qwen3-VL multimodal model support by enabling fused weight loading without shard_id and implementing stateless JIT precompilation on TPU, which improved deployment readiness and runtime performance. Refactored the weight loader API to use keyword arguments for shard_id and expert_id, increasing code clarity and reducing integration risk. Utilized Python, JAX, and PyTorch throughout the development process.