Nvidia Senior Solutions Architect - AI Infrastructure Engineer 

United States, Texas 

359379672

What you’ll be doing:

• Collaborate as a key technical advisor for the design, build-out, and optimization of university-level research computing infrastructures powering 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 management tools to help optimize resource utilization, and gain insight into 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 researcher on a whiteboard

• Collaborate with researchers to gather feature requests and product feedback for product and engineering teams

What we need to see:

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

• 8+ 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

• Experience with container tools (Docker, Singularity) including at-scale deployment of containerized environments

• Expertise in systems monitoring, telemetry, and systems performance optimization of research computing environments. Familiarity with tools such as Prometheus, Grafana, and 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:

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

• Experience deploying and evaluating cluster performance using benchmarks such as MLPerf and/or HPL.

• Experience in delivering technical training, workshops, or mentoring researchers on using HPC/AI 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