We are looking for a Senior Solutions Architect specializing in Data Center Systems & Performance to join our elite solutions architecture team. In this role, you will work at the intersection of groundbreaking hardware and complex software stacks. As a Solutions Architect, you will act as a pivotal technical expert uniting engineering, field teams, and customers with highly intensive requirements. You will be responsible for analyzing and optimizing the performance of world-class AI, deep learning, and HPC ecosystems. Come join us!
What you'll be doing:
Work together with our partners and customers to identify, analyze, and resolve complex performance bottlenecks across interconnected GPU, CPU, and networking systems.
Complete and maintain robust performance benchmarking suites to stress-test high-performance clusters and establish performance baselines.
Apply industry-standard performance tools to monitor hardware performance counters and extract deep system telemetry.
Deeply investigate system and software configurations to find and fix subtle discrepancies that impact peak performance.
Partner closely with internal engineering units and outside collaborators and customers to collectively develop solutions and boost infrastructure performance.
What we need to see:
BS or MS in Engineering, Electrical Engineering, Physics, or Computer Science (or equivalent experience).
8+ years of work-related experience in the high-tech industry, particularly in system build, performance analysis, and technical customer-facing roles.
A strong understanding of how CPUs, GPUs, and high-speed networking fabrics interact within massive clusters.
Practical experience with performance counters, profiling tools, and telemetry collection systems (e.g., Perf, eBPF, Prometheus, Grafana).
Practical experience working with containers, cloud provisioning, and scheduling tools such as Docker, Docker Swarm, Kubernetes, SLURM, Ansible.
Proven track record of transforming raw logs and telemetry into structured time series data, dashboards, and heat maps.
The ability to translate complex, low-level technical performance anomalies into clear, actionable narratives for cross-functional teams.
Strong collaborative skills and a proven history of building successful relationships across diverse engineering and operations teams.
Ways to stand out from the crowd:
Deep knowledge of multi-GPU communication libraries like NCCL, and how they optimize inter-GPU topologies.
Deep, hands-on experience working directly with NVIDIA hardware architectures, NVLink, NVSwitch, or NVIDIA Nsight tools.
Practical experience optimizing distributed AI training workloads, LLMs, or large-scale high-performance computing environments.
Experience developing or integrating Agentic AI frameworks to autonomously parse telemetry logs, diagnose configuration drifts, or automate cluster triage.
You will also be eligible for equity and benefits.
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.Skills Required
- BS or MS in Engineering, Electrical Engineering, Physics, or Computer Science (or equivalent experience)
- 8+ years work-related experience in high-tech industry, system build, performance analysis, and technical customer-facing roles
- Strong understanding of CPU, GPU, and high-speed networking interactions within large clusters
- Practical experience with performance counters, profiling tools, and telemetry collection systems (e.g., Perf, eBPF, Prometheus, Grafana)
- Practical experience with containers, cloud provisioning, and scheduling tools (e.g., Docker, Docker Swarm, Kubernetes, SLURM, Ansible)
- Proven track record transforming raw logs and telemetry into structured time series data, dashboards, and heat maps
- Ability to translate complex low-level technical performance anomalies into clear, actionable narratives for cross-functional teams
- Strong collaborative skills and history of building successful relationships across engineering and operations teams
- Deep knowledge of multi-GPU communication libraries like NCCL
- Hands-on experience with NVIDIA hardware architectures, NVLink, NVSwitch, or NVIDIA Nsight tools
- Practical experience optimizing distributed AI training workloads, LLMs, or large-scale HPC environments
- Experience developing or integrating Agentic AI frameworks for autonomous telemetry parsing, diagnosis, or cluster triage
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
