Mao contributed to backend and system-level improvements across the NVIDIA/KAI-Scheduler and langchain-ai/langgraph repositories. In langgraph, Mao enhanced documentation clarity by correcting grammar and clarifying workflow node descriptions using Markdown, reducing onboarding confusion. For NVIDIA/KAI-Scheduler, Mao delivered a Go-based performance optimization by refactoring cluster information caching, removing unnecessary deepcopies to lower memory allocations and improve scheduling throughput for large clusters. Additionally, Mao addressed a bug in MIG resource scheduling, refining GPU allocation logic to ensure accurate resource management on MIG-enabled nodes. The work demonstrated depth in system programming, Kubernetes scheduling, and GPU resource management, with careful attention to maintainability and performance.