Senior Software Solutions Engineer

Overview

Support NVIDIA AI Enterprise customers and deployments across cloud and datacenter environments, combining hands-on customer issue resolution with software development of supportability features, automation, diagnostics and deployment tooling.

What you'll do

• Support, triage and resolve complex customer software issues end-to-end.
• Develop and maintain product-facing features and deployment assets for AI Enterprise supportability, such as scripts, configuration guidance, Kubernetes manifests/Helm charts, and reproducible test cases.
• Develop and maintain Python-based tooling and automation (validators, log collectors, repro harnesses) to improve NVIDIA AI Enterprise deployment reliability across NGC and container orchestrators such as 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 or 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.

What you'll need

• BS in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience).
• 5+ years of system software development and troubleshooting experience, ideally with some customer-facing work.
• Strong computer science fundamentals and programming/scripting skills: Python required; Bash required.
• 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.
• Professional-level communication skills and interpersonal skills with a passion for solving problems.

Nice to have

• Go or C++ programming skills.
• Working knowledge of Windows.
• 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 or tools (e.g., Cursor, Claude Code, Codex, or similar) to accelerate debugging, automation, and test creation.
• Experience operating Kubernetes-based platforms in production, including cluster operations, upgrades, and 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.