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including workload schedulers, storage systems, and distributed compute nodes. Applies analytical methods to evaluate system performance, identify bottlenecks, and implement corrective actions to improve
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of computational computing clusters including, but not limited to GPU clusters, General HPC clusters, and SMP environments. Experience with Centos or similar Linux distributions. Experience with programming jobs
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environments, token-based data-access infrastructures, and next-generation HTTP/S caching technologies. The Lab also maintains the ATLAS distributed analytics and AI-assisted observability and operations
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, debugging), including CI/CD, containerization (Docker, Kubernetes), and robust debugging techniques. Experience with cloud platforms (AWS, GCP, or Azure) and parallel computing, with a focus on cost-efficient
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plant online and in parallel with PG&E. Monitors and controls load changes on the campus critical feeder and high voltage distribution system. Assists campus electricians and mechanics in instances
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of AI models and HPC applications, focusing on GPU-enabled computing. Implement parallel processing, distributed computing, and resource management techniques for efficient job execution. Integration and
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the responsibility for architecting, designing, and developing software application using web technologies, distributed computation and storage frameworks, data collection nodes, and sophisticated
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: The Software Systems Engineer will have the responsibility for architecting, designing, and developing software application using web technologies, distributed computation and storage frameworks, data collection
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remains an active subject-matter expert in HPC frameworks, distributed computing, AI-accelerated software stacks, and large-scale workflow orchestration. The role supports faculty, research staff, and
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning