Are you an experienced systems architect with an interest in advancing artificial intelligence (AI) and high-performance computing (HPC) in academic and research environments? We are looking for a Solutions Architect to join the higher education and research team! In this role you will work with universities and research institutions to optimize the design and deployment of AI infrastructure. Our team applies expertise in accelerated software and hardware systems to help enable groundbreaking advancements in AI, deep learning, and scientific research. This role requires a strong background in building and deploying research computing clusters, deploying AI and HPC workloads, and optimizing system performance at scale.What you’ll be doing:<ul><li>Collaborate as a key technical advisor for the design, build-out, and optimization of university-level research computing infrastructures powering GPU-accelerated scientific workflows</li><li>Work with university research computing to optimize hardware utilization with software orchestration tools such as NVIDIA Base Command, Kubernetes, Slurm, and Jupyter notebook environments</li><li>Implement systems monitoring and management tools to help optimize resource utilization, and gain insight into most demanding application workloads at research computing centers</li><li>Document what you learn. This can include building targeted training, writing whitepapers, blogs, and wiki articles, and working through hard problems with a researcher on a whiteboard</li><li>Collaborate with researchers to gather feature requests and product feedback for product and engineering teams</li></ul>What we need to see:<ul><li>BS/MS/PhD in Engineering, Mathematics, Physical Sciences, or Computer Science (or equivalent experience).</li><li>8+ years of relevant work experience</li><li>Strong experience in designing and deploying GPU-accelerated computing infrastructure</li><li>In-depth knowledge of cluster orchestration and job scheduling technologies, e.g. Slurm, Kubernetes, Ansible and/or Open OnDemand</li><li>Experience with container tools (Docker, Singularity) including at-scale deployment of containerized environments</li><li>Expertise in systems monitoring, telemetry, and systems performance optimization of research computing environments. Familiarity with tools such as Prometheus, Grafana, and NVIDIA DCGM</li><li>Understanding of datacenter networking technologies (InfiniBand, Ethernet, OFED) and experience with network configuration</li><li>Familiarity with power and cooling systems architecture for data center infrastructure</li></ul>Ways to stand out from the crowd:<ul><li>Background in deploying LLM training and inference workflows in a research computing environment</li><li>Experience deploying and evaluating cluster performance using benchmarks such as MLPerf and/or HPL.</li><li>Experience in delivering technical training, workshops, or mentoring researchers on using HPC/AI systems.</li><li>Applications and systems-level knowledge of OpenMPI and NCCL</li><li>Experience with debugging and profiling tools. E.g. Nsight Systems, Nsight Compute, Compute Sanitizer, GDB or Valgrind</li></ul>Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.<div><div><div><div><div><div><div>You will also be eligible for equity and <a href="https://www.nvidia.com/en-us/benefits/" target="_blank">benefits</a>.</div></div></div></div></div></div></div>Applications for this job will be accepted at least until July 29, 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.