Software Solutions Engineer

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

BS in Computer Science, Electrical Engineering, Computer Engineering, or related field (or equivalent experience)

At least 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

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