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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
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Listed pay typically runs
$184,000–$287,500
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The NCX Senior Engineer joins the DSX team to lead NVIDIA Cloud Partner Day 2 operational readiness. This role involves collaborating with strategic partners to build systems, procedures, and automation for managing large-scale NVIDIA accelerated infrastructure in production. You will develop methods to continuously validate GPU, CPU, storage, and network health across AI clusters to identify degraded infrastructure before it impacts training or inference workloads. Key responsibilities include establishing observability for InfiniBand/RoCE networking, Kubernetes, and storage, while building automated detection and remediation workflows. You will manage fleet lifecycle administration, including driver updates, OS patching, and configuration drift identification. Required skills include experience with Linux-based distributed systems, Kubernetes, and production observability. Candidates should possess programming proficiency in Python, Go, or shell scripting to support complex cloud infrastructure operations.
Lead Day 2 operational readiness for NVIDIA Cloud Partners to manage large-scale accelerated infrastructure.
Develop and implement methods to continuously validate GPU, CPU, storage, and network health across AI clusters.
Establish comprehensive telemetry, monitoring, alerting, and dashboards for compute, networking, and Kubernetes workloads.
Build automated workflows to detect, isolate, repair, and return unhealthy infrastructure to service.
Implement scalable strategies for managing GPU fleet lifecycles, including driver updates, firmware, and OS patching.
Translate NVIDIA reference architectures into production runbooks, automation, and measurable operational standards.
Develop reusable operational frameworks, tooling, and implementation guides for multiple partner environments.
Define health signals, SLOs, and acceptance criteria to provide clear insight into infrastructure reliability.
What we're looking for
BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
8+ years of experience in infrastructure engineering, Site Reliability Engineering, DevOps, cloud platform engineering, systems engineering, or similar roles.
Strong experience operating Linux-based distributed systems and cloud infrastructure in production.
Deep understanding of Kubernetes, containers, cluster scheduling, and the operational lifecycle of large multi-node environments.
Strong understanding of production observability, including metrics, logging, alerting, dashboards, health checks, and operations guided by service level agreements.
Experience crafting automation for infrastructure lifecycle management, failure detection, remediation, upgrades, and configuration management.
Strong networking fundamentals and experience troubleshooting complex distributed systems across compute, network, and storage layers.
Programming and automation experience using Python, Go, shell scripting, or similar languages.
Remote (Santa Clara, CA)+154 days ago
$184,000–$287,500
Python
Go
PyTorch
TensorFlow
Kubernetes
MLOps
Terraform
Ansible
Prometheus
Grafana
OpenTelemetry
CUDA
Triton
InfiniBand
RoCE
CI/CD
GitOps
Linux
Distributed Systems
VLSI Physical Design
RTL2GDS
Place and Route
Timing Closure
Power and Clock Distribution
Physical Verification
IR Drop Analysis
RC Extraction
Synopsys (ICC2
Cadence (Genus
Python
Perl
Tcl
Make
ML
AI