Senior Engineer, NCX

NVIDIA is a leading technology company specializing in Artificial Intelligence, High-Performance Computing, and Visualization. The role is within the DSX team, focusing on NVIDIA Cloud Partner (NCP) infrastructure operations to support large-scale NVIDIA accelerated infrastructure in production.

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or related field, or equivalent experience.
• 8+ years experience in infrastructure engineering, Site Reliability Engineering, DevOps, cloud platform engineering, or systems engineering supporting large-scale production environments.
• Strong experience operating Linux-based distributed systems and cloud infrastructure in production.
• Deep understanding of Kubernetes, containers, cluster scheduling, and operational lifecycle of large multi-node environments.
• Strong knowledge of production observability including metrics, logging, alerting, dashboards, and health checks.
• Experience automating infrastructure lifecycle management, failure detection, remediation, upgrades, and configuration management.
• Strong networking fundamentals and experience troubleshooting distributed systems across compute, network, and storage layers.
• Programming and automation skills using Python, Go, shell scripting, or similar languages.

Nice to have:

• Experience managing GPU or accelerated computing infrastructure for AI training and inference.
• Experience with NVIDIA technologies such as DGX/HGX systems, CUDA, NVLink/NVSwitch, NVIDIA networking, InfiniBand, RoCE, GPU Operator, Network Operator.
• Experience collaborating with NVIDIA Cloud Partners, hyperscale cloud providers, or managed AI clouds.
• Knowledge of infrastructure observability tools including Prometheus, Grafana, OpenTelemetry, Alertmanager.
• Knowledge of failure modes related to large distributed AI workloads.

• Lead NCP Day 2 operational readiness efforts in collaboration with NVIDIA Cloud Partners.
• Build and implement continuous infrastructure validation for GPU, CPU, storage, and network health.
• Establish observability and operational telemetry across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads.
• Develop automated detection and remediation workflows to manage unhealthy infrastructure.
• Refine fleet lifecycle administration including NVIDIA driver and firmware, Kubernetes node maintenance, OS patching, and configuration management.
• Operationalize NVIDIA reference architectures into production practices, automation, and measurable standards.
• Define operational health and readiness through health signals, SLOs, metrics, and validation mechanisms.
• Build reusable operational frameworks, including tooling, runbooks, and playbooks applicable across multiple NCP environments.