Senior HPC Cluster Engineer

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We are seeking a highly skilled and experienced HPC Cluster Engineer to design, deploy, and operate GPU Compute Clusters for EDA (Electronic Design Automation) and high-performance computing workloads used across multiple teams and projects. Join our engineering team and collaborate with researchers and infrastructure teams to ensure our GPU clusters are highly performant, scalable and reliable. What you'll be doing: Develop and enhance our ecosystem around GPU-accelerated computing including developing scalable automation solutions. Continuously improve infrastructure provisioning, management, observability and day to day operation through automation. Provide technical leadership and strategic guidance for managing large-scale HPC systems, including the deployment of compute, networking, and storage. Foster strong customer and multi-functional partnerships to ensure consistent cluster support and rapidly adapt to evolving user needs Support our researchers to run their EDA workloads including performance analysis and optimizations. Conduct root cause analysis and suggest corrective action. Proactively find and fix issues before they occur. Build innovative tooling to accelerate researchers' velocity, debugging and software performance at scale. What we need to see: Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience. Minimum of 5 years of proven experience crafting and operating large scale compute infrastructure, including cluster configuration managements tools such as BCM or Ansible. Experience with AI/HPC job schedulers and orchestrators, such as Slurm, LSF, PBS or K8s. Applied experience with AI/HPC workflows that use MPI and NCCL. Proficient in using Linux including Rocky/Centos/RHEL and/or Ubuntu Linux distributions. A solid understanding of container technologies such Enroot and Docker. Proficiency in Python and Bash Experience analyzing and tuning performance for a variety of EDA workloads. Excellent problem-solving to analyze complex systems, identify bottlenecks, and implement scalable solutions. Excellent communication and collaboration skills, with the ability to work effectively with various teams and individuals. Passion for continual learning and staying ahead of new technologies and effective approaches in the HPC infrastructure fields. Ways to stand out from the crowd: Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking. Experience supporting EDA workloads and tools. Familiarity with High-Speed Networking pertaining to HPC including InfiniBand, RDMA and RoCE. Understanding of fast, distributed storage systems such as Lustre and GPFS for AI/HPC workload. Familiarity with metrics collection and visualization at scale with Prometheus, OpenSearch and Grafana. Our technology has no boundaries! NVIDIA is building the most groundbreaking and powerful compute platforms for the world to use. It’s because of our work that scientists, researchers and engineers can advance their ideas. At its core, our visual computing technology not only enables an amazing computing experience, but it is also energy efficient! We pioneered a supercharged form of computing loved by the most demanding computer users in the world - scientists, designers, ar ... (truncated, view full listing at source)