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scalable compute solutions integrating CPU/GPU resources, high-speed interconnects, parallel storage, scheduling, and supporting infrastructure. Establish technical standards, architectures, and engineering
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and developing sustainable engineering solutions. Evaluate and introduce emerging technologies, architectures, and engineering approaches, including HPC, AI/ML, GPU computing, containers, and other
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requirements into scalable infrastructure solutions. Eligibility for access to Sensitive or Special Access Program information. Current Top Secret clearance with SCI eligibility. Preferred Qualifications
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coordinating delivery across infrastructure, security, and scientific programs. Success requires deep expertise in modern HPC system architecture, GPU-centric platforms, cluster scheduling, Linux at scale, and
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, or other highly regulated computing environments. HPC, AI/ML infrastructure, GPU computing, containers, Kubernetes, or advanced computing platforms. Infrastructure-as-code, advanced automation, CI/CD
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documentation of HPC architectures, configurations, and operational procedures. Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects
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on program needs. Basic Qualifications: BS degree in computer science, computer engineering, information systems or a related field, and 5 years of relevant professional experience (or an equivalent
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with ORNL research organizations to enable research excellence and delivery. We work with other clustered computing and HPC groups to help research programs identify the best solutions for their needs
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participation in development of underlying libraries for performance portability. A strong background in particle based and statistical modeling is preferred. Competency in programming across hardware types
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projects, deploying workflows on large-scale computing environments, leveraging ORNL’s Frontier for its dense GPU-based high-performance computing resources to train large GeoAI models. Major Duties