Job description

NVIDIA is looking for a hands-on Solutions Architect to raise the Day 2 operations bar across our NVIDIA Cloud Partner ecosystem. Day 2 starts when a cluster is installed and validated: keeping the service healthy, adapting it as technology and customer demand change, and improving performance, stability, efficiency and economics over time. You will work with engineers running AI clouds at scale on the problems that decide whether customers stay and whether the next generation of NVIDIA technology lands successfully. Our job is to work hand in hand with NCPs to solve real problems and drive real optimizations, prove the answer, and turn it into something the next partner can use! This is not an outsourced operations role. The partner owns its cloud; success means leaving its team more capable, not more dependent on ours. What you'll be doing: • Solve hard Day 2 operations problems at scale. Work alongside partner engineers to find the cause, prototype an approach, validate it under representative load, and leave behind a practice their team can operate. • Make new technology Day 2 ready. Help partners prepare the operating model for new NVIDIA platforms, capacity, services, and use cases before customers depend on them, and help drive adoption in live environments without degrading service. • Improve reliability, performance, and economics together. Use measures such as incident frequency, recovery time, utilization, and cost per token to show where the cloud is losing performance or margin - and whether the fix worked. • Raise each partner's Day 2 maturity. Identify and help close the gaps that matter across people, process, tooling, telemetry, security, and incident response. • Turn one solution into ecosystem capability. Convert validated work into operating procedures, reference architectures, assessments, automation, and agentic workflows that other NCPs can integrate into their standard operating model. • Create the feedback loop only NVIDIA can. Spot patterns across partners early and bring clear field evidence to account teams, support, product, and engineering so repeated problems are fixed at the right level. What we need to see: • BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Physics, Mathematics, or a related field - or equivalent experience. • 12+ years in production infrastructure, cloud engineering, solutions architecture, site reliability engineering, HPC, or a similar technical role; alternatively, 5+ years of exceptional specialist-level work in large-scale GPU or AI infrastructure. • Experience building, operating, or improving distributed infrastructure under real production load - not only designing or deploying it. • Deep expertise in at least one part of the Day 2 stack, backed by hands-on work with large-scale GPU, HPC, or cloud infrastructure. Relevant technologies may include DCGM, BMC/Redfish, and firmware and driver lifecycle; InfiniBand or high-speed Ethernet, NCCL, and UFM; or high-performance storage such as Lustre, IBM Storage Scale, WEKA, VAST Data, or comparable platforms. • Working experience across the broader operating platform, including Kubernetes or Slurm, GPU scheduling and multi-tenancy, Prometheus, Grafana or OpenTelemetry, and automation with Terraform, Ansible, Argo CD, or similar tooling. • Strong Linux knowledge and enough Python, Bash, or similar experience to automate measurement, diagnosis, validation, or remediation. • A detailed evidence-led approach to troubleshooting across system boundaries, paired with the judgment to make difficult technical findings clear. • The ability to lead sophisticated work with partner engineers and cross-functional teams without direct authority or taking ownership away from the operator. • Strong communication, prioritization, and time-management skills across multiple partner engagements. Ways to stand out from the crowd: • Real world experience operating a GPU cloud, HPC environment, or large-scale AI platform under customer load. • Built or matured a 24/7 operations function, including observability, incident and problem management, coverage, and on-call design. • Hands on experience with NVIDIA rack-scale platforms such as GB200 or GB300 NVL72 into production, or have hands-on experience with NVIDIA operations technologies such as Spectrum-X, UFM, Base Command Manager, Mission Control, and the GPU or Network Operators. • Driven improved fleet health or unit economics through benchmarking, infrastructure as code, GitOps, automated diagnosis, or agent-based remediation. Even if your background doesn't match every line above, we'd love to hear how your experience applies. With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. This role presents an opportunity to have a wide impact at NVIDIA by improving the factory planning function. Are you creative, hard-working, dedicated, and determined? Do you love a challenge? If so, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 17, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive 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.

Requirements

Must-haves

Solve hard Day 2 operations problems at scale. Work alongside partner engineers to find the cause, prototype an approach, validate it under representative load, and leave behind a practice their team can operate.
Raise each partner's Day 2 maturity. Identify and help close the gaps that matter across people, process, tooling, telemetry, security, and incident response.
Create the feedback loop only NVIDIA can. Spot patterns across partners early and bring clear field evidence to account teams, support, product, and engineering so repeated problems are fixed at the right level.
BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Physics, Mathematics, or a related field - or equivalent experience.
Experience building, operating, or improving distributed infrastructure under real production load - not only designing or deploying it.
Strong Linux knowledge and enough Python, Bash, or similar experience to automate measurement, diagnosis, validation, or remediation.
A detailed evidence-led approach to troubleshooting across system boundaries, paired with the judgment to make difficult technical findings clear.
The ability to lead sophisticated work with partner engineers and cross-functional teams without direct authority or taking ownership away from the operator.
Strong communication, prioritization, and time-management skills across multiple partner engagements.
Real world experience operating a GPU cloud, HPC environment, or large-scale AI platform under customer load.
Built or matured a 24/7 operations function, including observability, incident and problem management, coverage, and on-call design.
Driven improved fleet health or unit economics through benchmarking, infrastructure as code, GitOps, automated diagnosis, or agent-based remediation.
Even if your background doesn't match every line above, we'd love to hear how your experience applies.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 17, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aikubernetespython

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