Solutions Architect, Infrastructure

NVIDIA is looking for an Infrastructure Solutions Architect to lead deployment and bring-up of their next-generation Data Center GPUs and networking platforms.

As part of the NVIDIA Solutions Architecture team, you will navigate uncharted technical and organizational spaces , serving as the bridge between early platform readiness, cloud engineering teams, product strategy, and large-scale customer deployments.

Responsibilities:

• Lead end-to-end execution for Hyperscaler customers to rapidly bring NVIDIA Data Center GPU and networking platforms to market at scale.
• Drive strategic partnership and alignment with Product teams to understand roadmap intent, co-define critical metrics, and ensure unified direction across technical, sales, and leadership organizations.
• Influence without authority across Product, Engineering, Sales, Operations, and CSP customers, driving clarity, alignment, and unblock paths for scale-up.
• Analyze deployment and performance data, identifying product health trends, system bottlenecks, and operational risks.
• Solve challenging technical problems involving GPUs, networking, drivers, containers, firmware, and distributed system interactions.
• Deliver streamlined executive-level communication on status, risks, progress, and required decisions.
• Collaborate with Product and Engineering, enabling future improvements in platform design, validation, and operational workflows.

Requirements:

• BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, or similar, or equivalent experience.
• 4+ years experience in Solutions Architecture, Infrastructure Engineering, or similar technical roles.
• Hands-on experience with bring-up and validation of large-scale NVIDIA GPU platforms, including multi-GPU and multi-node architectures.
• Understanding of high-performance networking technologies (e.g., RDMA, congestion control, high-bandwidth interconnects) and their role in distributed AI workloads.
• Familiarity with NVIDIA system software stacks: CUDA, NCCL, NVSwitch/NVLink, driver behavior, and performance tuning.
• Proficiency with Linux systems tools for identifying issues and evaluating system performance, such as: dmesg, journalctl, lspci, numactl, ethtool, iostat, perf, nvidia-smi, top/htop, ipmitool, container-level tooling, and related utilities.
• Understanding of server hardware architecture, including PCIe topologies, system firmware, NUMA, BIOS/UEFI configuration, power/thermal envelopes, and memory/subsystem behavior.
• Understanding of BMC/IPMI/Redfish for remote management, hardware health monitoring, and out-of-band debugging during early-stage bring-up.
• Strong Linux fundamentals across drivers, kernel subsystems, cgroups, containers, and node-level performance analysis.
• Ability to identify performance bottlenecks at the cluster, node, accelerator, network, or application layer.

Benefits:

• Equity
• Benefits