NVIDIA
AI/ML, High Performance Computing (HPC), Technology
SeniorAIandMLHPCClusterEngineer
“Senior AI and ML HPC Cluster Engineer at NVIDIA. Skills: AI/ML HPC Cluster Engineering, GPU Compute Clusters, Large-scale infrastructure design and operation, Automation, Performance analysis and tuning. 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 solutions”
Industry & Context.
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.; Root cause analysis; Suggest corrective action; Proactively find and fix issues
What They're Looking For.
Must Have
Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience, Minimum 5+ years of experience designing and operating large scale compute infrastructure, Experience with AI/HPC advanced job schedulers, such as Slurm, K8s, PBS, RTDA or LSF, Proficient in administering Centos/RHEL and/or Ubuntu Linux distributions, Solid understanding of cluster configuration managements tools such as Ansible, Puppet, Salt, In depth understating of container technologies like Docker, Singularity, Podman, Shifter, Charliecloud, Proficiency in Python programming and bash scripting, Applied experience with AI/HPC workflows that use MPI, Experience 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
Nice to Have
Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking, Experience with Machine Learning and Deep Learning concepts, algorithms and models, Familiarity with InfiniBand with IPoIB and RDMA, Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads, Familiarity with deep learning frameworks like PyTorch and TensorFlow
What You'll Do.
Provide leadership and strategic guidance on the management of large-scale HPC systems including the deployment of compute
Develop and improve our ecosystem around GPU-accelerated computing including developing scalable automation solutions
Build and maintain AI and ML heterogeneous clusters on-premises and in the cloud
Support our researchers to run their workloads including performance analysis and optimizations
Conduct root cause analysis and suggest corrective action
Proactively find and fix issues before they occur
How You'll Work.
Team & Collaboration
Create and cultivate customer and cross-team relationships to reliably sustain the clusters and meet user evolving user needs
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