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 AI/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 a technical leader 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:Provide leadership and strategic guidance on the management of large-scale HPC systems including the deployment of compute, networking, and storage.Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutionsBuild and maintain AI and ML heterogeneous clusters on-premises and in the cloudCreate and cultivate customer and cross-team relationships to reliably sustain the clusters and meet user evolving user needsSupport our researchers to run their workloads including performance analysis and optimizationsConduct root cause analysis and suggest corrective action Proactively find and fix issues before they occurWhat we need to see:Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experienceMinimum 5+ years of experience designing and operating large scale compute infrastructureExperience with AI/HPC advanced job schedulers, such as Slurm, K8s, PBS, RTDA or LSFProficient in administering Centos/RHEL and/or Ubuntu Linux distributionsSolid understanding of cluster configuration managements tools such as Ansible, Puppet, SaltIn depth understating of container technologies like Docker, Singularity, Podman, Shifter, CharliecloudProficiency in Python programming and bash scriptingApplied experience with AI/HPC workflows that use MPIExperience analyzing and tuning performance for a variety of AI/HPC workloads.Passion for continual learning and staying ahead of emerging technologies and effective approaches in the HPC and AI/ML infrastructure fields.Ways to stand out from the crowd:Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarkingExperience with Machine Learning and Deep Learning concepts, algorithms and modelsFamiliarity with InfiniBand with IBOP and RDMAUnderstanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloadsFamiliarity with deep learning frameworks like PyTorch and TensorFlowYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 136,000 USD - 212,750 USD for Level 3, and 168,000 USD - 264,500 USD for Level 4.You will also be eligible for equity and benefits.Applications for this job will be accepted at least until October 22, 2025.NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.