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ensemble structures. As an Empire AI-funded fellow, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This
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research computing ecosystem spanning on-premises and remote-site infrastructure, including: • HPC compute platforms for research and data-intensive workloads • GPU-enabled environments for AI and machine
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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship
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, and security monitoring tools. PREFERRED: Professional certification (CISSP or equivalent), hands-on experience with securing HPC, GPU cluster, or data center environments, experience with AI/ML
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maintain the workload scheduler and architect quality-of-service policies. Administer Linux systems across infrastructure projects and deployment of new GPUs for research and teaching. Troubleshoot complex
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, regional, and national professional meetings, workshops, and conferences. The Machine Learning Engineer will have the opportunity to work with leading-edge GPU and HPC technologies and engage with domain
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and will analyze
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operation of Horizon, NSF’s next-generation leadership-class GPU system based on NVIDIA accelerator technologies. Horizon will significantly expand TACC’s capabilities in large-scale simulation, data
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
performance of complex AI research workloads on state-of-the-art hardware. The role will have heavy focus on optimizing existing NVIDIA GPU-based workloads for top-tier AMD GPUs, such as MI355X and beyond and
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managing GPU-enabled infrastructure (NVIDIA GPUs, CUDA, multi-GPU systems) in cloud and/or on-prem environments. Familiarity with GPU orchestration in Kubernetes (e.g., NVIDIA device plugin, GPU scheduling