NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Make the choice to join us today!
As a member of the GPU/HPC Infrastructure team, you will provide leadership in the design and implementation of ground breaking GPU compute clusters that run demanding deep learning, high performance computing, and computationally intensive workloads. We seek an expert to identify architectural changes and/or completely new approaches for our GPU Compute Clusters. As an expert, you will help us with the strategic challenges we encounter including: compute, networking, and storage design for large scale, high performance workloads, effective resource utilization in a heterogeneous compute environment, evolving our private/public cloud strategy, capacity modeling, and growth planning across our global computing environment.
What you'll be doing:
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Building and improving our ecosystem around GPU-accelerated computing including developing large scale automation solutions
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Maintaining and building deep learning clusters at scale
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Supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows
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Root cause analysis and suggest corrective action for problems large and small scales
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Finding and fixing problems before they occur
What we need to see:
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Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
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Minimum 5 years of experience designing and operating large scale compute infrastructure.
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Experience analyzing and tuning performance for a variety of HPC workloads.
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Working knowledge of cluster configuration managements tools such as Ansible, Puppet, Salt.
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Experience with HPC cluster job schedulers such as SLURM, LSF
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In depth understating of container technologies like Docker, Singularity, Shifter, Charliecloud
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Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting
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Experience with HPC workflows that use MPI
Ways to stand out from the crowd:
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Understanding of MLPerf benchmarking
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Familiarity with InfiniBand with IBOP and RDMA
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Understanding of fast, distributed storage systems like Lustre and GPFS for HPC workloads.
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Background with Software Defined Networking and HPC cluster networking
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Familuarity with deep learning frameworks like PyTorch and TensorFlow
NVIDIA offers highly competitive salaries and a comprehensive benefits package. We have some of the most brilliant and talented people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with real passion for technology, we want to hear from you.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
