10 days ago
Base Salary
$224k - $357k/yr
Responsibilities
- Solve large-scale Day 2 operations problems alongside partner engineers through root-cause analysis, prototyping, load validation, and operational handoff.
- Prepare new NVIDIA platforms, capacity, services, and use cases for production adoption without degrading service.
- Improve reliability, performance, utilization, recovery time, incident frequency, and cost per token across partner clouds.
- Assess and improve partner maturity across people, process, tooling, telemetry, security, and incident response.
- Convert validated solutions into operating procedures, reference architectures, assessments, automation, and agentic workflows.
- Identify recurring patterns across partners and provide field evidence to account teams, support, product, and engineering.
Requirements
- 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.
- Deep hands-on expertise in at least one area of large-scale GPU, HPC, or cloud infrastructure, such as GPU management, firmware and drivers, high-speed networking, NCCL, or high-performance storage.
- Experience with Kubernetes or Slurm, GPU scheduling and multi-tenancy, Prometheus, Grafana or OpenTelemetry, and automation tooling.
- Strong Linux knowledge and sufficient Python, Bash, or similar experience for automation and diagnosis.
- Evidence-led troubleshooting skills across system boundaries and the ability to communicate difficult technical findings clearly.
- Ability to lead sophisticated work with partner engineers and cross-functional teams without direct authority.
- Strong communication, prioritization, and time-management skills across multiple partner engagements.
- Preferred experience operating GPU clouds, HPC environments, or large-scale AI platforms under customer load; building 24/7 operations functions; deploying NVIDIA rack-scale platforms; or improving fleet health and unit economics through infrastructure as code, GitOps, automated diagnosis, or agent-based remediation.
Benefits
- Competitive salary and a generous benefits package.
- Eligible for equity and benefits.
- Applications accepted at least until November 3, 2026.
Tech Stack
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.