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for operating and developing a research infrastructure comprising CPU- and GPU-based HPC systems, petabyte-scale storage, scientific web services, secure environments for sensitive data, and emerging AI-related
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or GPU computing environments. Familiarity with version control, containerization, and reproducible-research tooling. Preferred Competencies Work independently and collaboratively within a
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microscopy, X-ray imaging, and light microscopy). It supports researchers across three compute scales: laptop-based prototyping (S-Gym), single-GPU workstations (M-Gym), and multi-node HPC at the National
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skills include the following: Applicants must demonstrate knowledge of Linux-based computing, high-performance computing, GPU-enabled environments, distributed or cluster-based systems, and AI
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project GPUs (B200s and cloud credits) and GPUs from the Department of Computing and the College of Engineering (A100s and H200s) The opportunity to continue your career at a world-leading institution and
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Position Summary Serve as the Lead for the team ensuring smooth operation of the Linux cluster consisting of 300+ GPU/CPU compute nodes including parallel filesystems and high-performance network
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 5 hours ago
supporting GPU computing environments, including NVIDIA drivers, CUDA, GPU scheduling, monitoring, and performance optimization; familiarity with AMD ROCm is desirable. Experience with container technologies
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, AI agent orchestration platforms and GPU-enabled AI infrastructure. Responsibilities: Platform operations and reliability Own day-to-day operations of SEA-LION API Farm, our multi-cloud LLM inference
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performance GPU computing — whether through thousands of commercial GPUs or a handful of Nvidia NVL72 racks — and specialized signal processing hardware. Automation: Develop and implement automated processes
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programming in C/C++, preferably also Rust, and of POSIX, the Linux/Unix kernel or RTOS. Documented competence in parallel and distributed systems, including GPU programming (e.g. CUDA). Ability to explain