Solutions Architect, Infrastructure - Research Computing

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 workloads, and optimizing system performance at scale.

What you’ll be doing:

• Technical advisor for the design, build-out, and optimization of university-level research computing infrastructures that include GPU-accelerated scientific workflows.

• Work with university research computing to optimize hardware utilization with software orchestration tools such as NVIDIA Base Command, Kubernetes, Slurm, and Jupyter notebook environments.

• Implement systems monitoring and telemetry tools to help optimize resource utilization, and track most demanding application workloads at research computing centers.

• Document what you learn. This can include building targeted training, writing whitepapers, blogs, and wiki articles, and working through hard problems with a customer on a whiteboard.

• Provide customer requirements and feedback to product and engineering teams.

What we need to see:

• MS or PhD in Engineering, Mathematics, Physical Sciences, or Computer Science (or equivalent experience).

• 5+ years of relevant work experience.

• Strong experience in designing and deploying GPU-accelerated computing infrastructure.

• In-depth knowledge of cluster orchestration and job scheduling technologies, e.g. Slurm, Kubernetes,Ansible and/or Open OnDemand. And experience with container tools (Docker, Singularity, Enroot/Pyxis) including at-scale deployment of containerized environments

• Expertise in systems monitoring, telemetry, and systems performance optimization of research computing environments. Familiarity with tools like Prometheus, Grafana or NVIDIA DCGM.

• Understanding of datacenter networking technologies (InfiniBand, Ethernet, OFED) and experience with network configuration.

• Familiarity with power and cooling systems architecture for data center infrastructure.

Ways to stand out from the crowd:

• Experience in deploying LLM training and inference workflows in a research computing environment.

• Experience working with technical computing customers in the academic research computing space.

• Practical knowledge of high-performance parallel file systems.

• Applications and systems-level knowledge of OpenMPI and NCCL.

• Experience with debugging and profiling tools. E.g. Nsight Systems, Nsight Compute, Compute Sanitizer, GDB or Valgrind.

With highly competitive salaries, a comprehensive benefits package, and an excellent engineering work culture, NVIDIA is widely considered to be one of the industry's most desirable employers.