De Huo is a system software engineer based in Seattle who builds and optimizes large-scale GPU and cloud inference platforms. With a background spanning OCI compute and AI services at Oracle to Triton inference work at NVIDIA, he focuses on distributed model serving, GPU resource management, Kubernetes, and production-grade model training/serving pipelines. Proficient in Python, Rust, Go, C++, Java, and shell scripting, he brings a systems-first approach to cloud-native ML infrastructure and low-level performance engineering. Early work on hypervisor fleet CI/CD, image building and large fleet patching gives him practical expertise in reliability and automation for at-scale compute environments. Although relatively early in his career, he has rapidly progressed into senior roles driving LLM and CV serving initiatives across cloud and edge deployments. His cross-cutting experience—spanning virtual machine infrastructure to GPU-backed inference—makes him effective at bridging infra, runtime, and ML serving concerns.
1 year of coding experience
5 years of employment as a software developer
University of California Santa Cruz
Bachelor of Engineering Electrical Engineering and Automation, Bachelor of Engineering Electrical Engineering and Automation at Hohai University
The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Contributions:5 reviews, 1 PR, 73 pushes in 1 month
nvidia-dockernvidiadeep-learninggpuinference
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De Huo - Senior System Software Engineer at NVIDIA