Continuous Bring Up Solution Architect - NVIS

We are hiring a Solution Architect Student to customer-facing teams supporting different technical areas such as IB/ETH networking, DPU, Cloud infrastructure DevOps, HPC/AI workloads, and customer success. In the process, you will have the opportunity to become a specialist in our enterprise products including the DGX/HGX systems, and networking DPUs and switches, as well as our developer software platforms including NVIDIA Omniverse, NVIDIA HPC and NVIDIA AI. It may also be possible to rotate with our program management or engineering teams. Are you ready for the challenge?

What you’ll be doing:

Be responsible for the setup of experiments, tests, equipment, and otherwise facilitate evaluations that help solve customer problems using NVIDIA technologies
Partner with Build and Program Management teams to support AI Factory deliveries globally.
Establish close technical ties to the customer account, establishing personal relationships to facilitate rapid resolution of customer issues
Work closely and collaborate with the NVIDIA customer account team, other Solution Architects, and/or product engineering teams during quarterly rotation assignments
You will raise and provide timely advance warning of critical customer issues that require additional attention
Present platform solutions to customers, partners, community, etc.
Some rotation assignments might require up to 15% travel

What we need to see:

Bachelor’s in computer science/electrical/mechanical engineering.
1+ years of experience in data center infrastructure.
Knowledge in Data Sciences, Deep Learning, or Machine Learning
Strong programming skills in one or more high-level languages Python, C, C++, Rust, etc
Motivated self-starter with an equal balance of strong problem-solving skills and strong customer-facing communication skills including presenting
Strong collaboration and interpersonal skills
Passion for continuous learning, knowledge transfer and working in a dynamic environment without losing focus

Ways to stand out from the crowd:

Background with working on AI Deep Learning and Machine Learning Applications, AI Model Training/Inferencing or other GPU related technologies including using TensorFlow, PyTorch, DL frameworks or CUDA
Experience working with Docker, Kubernetes or slurm both on-prem and cloud-based infrastructure including HPC or AI supercomputer clusters
System, networking and storage hardware, software and administrative experience
Exposure to cloud service platforms such as AWS, Azure, GCP or OCI through coursework or through certification programs