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 on the planet working for us. If you're creative, passionate, and self-motivated, we want to hear from you!
We are looking for an experienced customer-facing software and systems engineer with a strong background in enterprise infrastructure, cloud technologies, virtualization, and Kubernetes. The ideal candidate combines hands-on software engineering and troubleshooting with the ability to understand customer requirements, lead technical discussions, and work across engineering teams to deliver practical solutions. You will join a team of Solution Engineers focused on NVIDIA Networking, DGX Platforms, Container Orchestrators, Deep Learning containers, and other Enterprise-related system software. The focus of this role is user-space networking software, containerized services, and management and diagnostic tools, including those used with SONiC and NVIDIA NVOS.
SW Solution Engineers spend approximately 50% of their time helping customers with their most complex problems and 50% doing R&D-related work. In this role, you will apply your experience across software, operating systems, networking, and cloud infrastructure to resolve customer issues and develop capabilities that make future investigations faster and more effective.
What you'll be doing:- Take ownership of enterprise customer issues involving user-space networking software, containerized services, and infrastructure integration across on-premises, cloud, virtualized, and Kubernetes environments, driving investigations from initial engagement through resolution.
- Work directly with customers to understand their architecture, workloads, and operational requirements. Lead technical discussions, structure troubleshooting activities, and communicate findings, recommendations, and next steps clearly.
- Collaborate with software, platform, and networking engineering teams to solve user-space issues, analyze service dependencies, configuration, and logs, and validate fixes across relevant software versions and deployment environments.
- Develop software tools and automation that improve the deployment, availability, reliability, and supportability of NVIDIA Enterprise solutions.
- Own the development and ongoing operation of NVEX Networking tools, dashboards and automation pipelines. Prioritize improvements, maintain code and test coverage, manage releases and documentation, and automate diagnostic collection, environment preparation, issue reproduction, validation, and reporting to accelerate customer issue resolution.
- Share troubleshooting knowledge, guide engineers through technical reviews, and help customers and internal teams adopt reliable deployment and operational practices.
- BSc in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience.
- 8+ years of experience across software engineering, systems engineering, or technical solution delivery, including hands-on development and troubleshooting of complex enterprise environments.
- Extensive experience working directly with enterprise customers, translating requirements into technical solutions, leading technical discussions, and communicating effectively with engineering and business stakeholders.
- Strong analytical and problem-solving skills, intellectual curiosity, self-motivation, and the ability to collaborate across technical disciplines and take issues through to resolution.
- A strong software engineering background, including development of tools and automation, source control, code reviews, automated testing, pipeline implementation, and flow/agent automation. Hands-on scripting experience.
- Practical experience with cloud and virtualized infrastructure, including compute, virtual networking, hybrid connectivity, deployment, and operations on platforms such as AWS or Azure.
- Hands-on experience with Kubernetes and containerized workloads, including deployment, configuration, monitoring, and troubleshooting of the supporting infrastructure.
- Experience working with Linux and Windows environments and troubleshooting application and service behavior, configuration, logs, resource usage, and network connectivity.
- Experience deploying, operating, and troubleshooting containerized applications with Docker, Kubernetes/EKS, or ECS, including service lifecycle, configuration, and dependencies.
- A record of developing reusable software tools, scripting, infrastructure as code, and engineering pipelines adopted across teams.
- Experience automating large-scale server or network environments, including provisioning, software and tool deployment, Linux/Windows compatibility matrices, software-version testing, and test-result aggregation.
- Experience implementing centralized logging, monitoring, and reporting with tools to support troubleshooting and operational visibility.
- Experience in production deployments, or complex troubleshooting engagements, together with technical workshops, mentoring, and cross-team collaboration.
Skills Required
- Bachelor of Science degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience
- 8+ years of experience in software engineering, systems engineering, or technical solution delivery
- Hands-on development and troubleshooting of complex enterprise environments
- Direct experience working with enterprise customers and translating requirements into technical solutions
- Ability to lead technical discussions and communicate effectively with engineering and business stakeholders
- Strong software engineering background, including tools and automation development, source control, code reviews, automated testing, pipeline implementation, and scripting
- Experience with cloud and virtualized infrastructure, compute, virtual networking, hybrid connectivity, deployment, and operations on AWS or Azure
- Hands-on experience with Kubernetes and containerized workloads, including deployment, configuration, monitoring, and infrastructure troubleshooting
- Experience troubleshooting Linux and Windows application and service behavior, configuration, logs, resource usage, and network connectivity
- Experience deploying, operating, and troubleshooting containerized applications with Docker, Kubernetes/EKS, or ECS
- Experience developing reusable software tools, scripting, infrastructure as code, and engineering pipelines adopted across teams
- Experience automating large-scale server or network environments, provisioning, software deployment, compatibility testing, and test-result aggregation
- Experience implementing centralized logging, monitoring, and reporting tools
- Experience with production deployments or complex troubleshooting engagements, technical workshops, mentoring, and cross-team collaboration
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.
-
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.
-
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.
-
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.”
