Developed hardware-aware tensor precision optimization for the FlagOpen/FlagGems repository, enabling efficient FP64 support by conditionally upcasting tensors only on hardware with FP64 capabilities. This approach improved memory efficiency and throughput while preventing unnecessary precision increases on unsupported devices. Leveraged Python for data processing and performance optimization, integrating unit testing to ensure robust functionality. Enhanced code quality by enforcing pre-commit checks and maintaining consistent code style, which contributed to maintainable and traceable changes. Collaborated closely with reviewers and contributors, using signed-off commits to ensure clear attribution and smooth integration of new features within the team’s engineering workflow.