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
What we need to see
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
Ways to stand out from the crowd
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
