Senior Software Solutions Engineer
NVIDIA · Posted Oct 12
Graphics processing units, artificial intelligence platforms, accelerated computing, and data center solutions
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About the role
The role supports NVIDIA AI Enterprise customers and deployments across cloud and datacenter environments, working across compute and cloud-native technologies in CSP environments including container platforms/orchestrators, enterprise system software, and GPU-accelerated AI frameworks and inference services used to run production AI workloads at scale. It is a dual role: supporting, triaging and resolving complex customer software issues end-to-end, and building software features, automation, diagnostics, reproducible test cases, and deployment tooling to improve product readiness and scale support. In this customer-facing position, the engineer works closely with customers and internal engineering teams to understand issues, explain root causes, drive resolution, and collaborate on fixes and improvements.
What you will do
- Develop and maintain product-facing features and deployment assets for AI Enterprise supportability (e.g., scripts, configuration guidance, Kubernetes manifests/Helm charts, and reproducible test cases)
- Develop and maintain Python-based tooling/automation (validators, log collectors, repro harnesses) to improve NVIDIA AI Enterprise deployment reliability across NGC and container orchestrators (e.g., Kubernetes)
- Contribute code-level fixes, patches, or pull requests (as appropriate) in collaboration with engineering to address customer-impacting issues and improve product readiness
- Support enterprise customers deploying NVIDIA AI Enterprise in datacenter and CSP environments, including Kubernetes-based and containerized production AI platforms
- Take ownership of customer issues from inception to resolution: reproduce in lab/cloud, collect diagnostics, provide mitigations, and partner with engineering on fixes
- Create high-quality bug reports and RFEs with clear repro steps, environment details (CSP/Kubernetes/GPU), impact analysis, and supporting artifacts
- Develop customer-facing and internal documentation (KBs, runbooks, deployment guidance) to improve time-to-value and reduce recurring issues
- Be on call one weekend per month in the event a customer has a Sev1 outage and requires engineering assistance
Skills used in this role
What the employer is looking for
- BS in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience)
- 5+ years system software development and troubleshooting experience, ideally with some customer facing
- Strong computer science fundamentals and programming/scripting skills (Python required; Bash; Go/C++ a plus) to automate investigations and build diagnostics/repro tools
- Strong troubleshooting fundamentals (networking, concurrency, OS concepts) and a structured approach to isolating issues across application, platform, and infrastructure layers
- Deep understanding of at least two of the following: data centers/servers, distributed systems, virtualization, deep learning frameworks, containers (Docker/Kubernetes), hybrid cloud (AWS/Azure/GCP), and CI/CD for reliable deployments
- Familiarity with GPU-accelerated AI/ML stacks and production model deployment/serving (e.g., NGC containers, CUDA/tooling concepts, inference serving such as Triton or similar)
- Deep Linux knowledge and comfort troubleshooting in production Linux environments; working knowledge of Windows is a plus
- Professional-level communication skills, interpersonal skills with a passion to solve problems
Preferred qualifications
- Hands-on experience deploying and operating NVIDIA AI Enterprise components in production across on-prem or CSP environments
- Hands-on experience using AI coding assistants/tools (e.g., Cursor, Claude Code, Codex, or similar) to accelerate debugging, automation, and test creation
- Experience operating Kubernetes-based platforms in production (cluster operations, upgrades, control-plane/data-plane failure modes)
- Strong performance debugging skills for GPU and cloud workloads (profiling, latency/throughput tuning) and familiarity with observability/tracing tools
Benefits and support
- Compensation and benefits are detailed in the job posting
About NVIDIA
NVIDIA Corporation is a multinational technology company that designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market. Pioneering accelerated computing, the company has become a driving engine of modern artificial intelligence, deep learning, and data center infrastructure.
- Industry
- Semiconductors
- Company size
- 42000+ employees
- Founded
- April 5, 1993
- Location
- Santa Clara, California, USA
- Funding stage
- Public Company
Funding
Public Company · $5M raised
Leadership
Founder, President and Chief Executive Officer
Executive Vice President and Chief Financial Officer
Founder and NVIDIA Fellow
Executive Vice President, Worldwide Field Operations
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