Senior Engineer, NCX

NVIDIA Germany; Deutschland; Vor Ort; EUR 120.000 - 180.000; Vollzeit

Zusammenfassung

NVIDIA is seeking an NCX Senior Engineer to strengthen Cloud Partner (NCP) infrastructure operations within the DSX team. You will guide partners beyond initial deployment into Day 2 operations, focusing on health, telemetry, lifecycle management, and rapid remediation to ensure reliable NVIDIA workloads at scale.

You will collaborate with partner engineering teams to implement scalable validation, observability, and automation across GPUs, CPUs, storage, and networking in large AI clusters,

Qualifikationen

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

Aufgaben

• Lead NCP Day 2 operational readiness efforts with NVIDIA Cloud Partners to set up systems, procedures, automation, and operating methods.
• Build continuous infrastructure validation for GPU, CPU, storage, and network health across large AI clusters.
• Establish observability and telemetry: monitoring, alerting, dashboards, and signals across compute, GPU, networking, storage, Kubernetes, and AI workloads.
• Develop automated detection and remediation workflows to restore unhealthy infrastructure with minimal disruption to workloads.
• Refine fleet lifecycle administration: driver/firmware management, Kubernetes node maintenance, OS patching, configuration drift, upgrades.
• Operationalize NVIDIA reference architectures into production runbooks, automation, and measurable standards.
• Define health signals, SLOs, metrics, acceptance criteria, and ongoing validation mechanisms for reliability and readiness.
• Build reusable operational frameworks, tooling, runbooks, playbooks, and reference implementations for multiple NCP environments.

Kenntnisse

Kubernetes experience

Linux distributed systems

Observability experience

Programming in Python/Go/shell

Cloud infrastructure experience

Networking fundamentals

Automation scripting

Collaboration with partners

Ausbildung

Bachelor's/Master's/PhD in CS or Engineering

Tools

Prometheus

Grafana

OpenTelemetry

Alertmanager

Docker

Jobbeschreibung

NVIDIA is hiring an NCX Senior Engineer who is passionate about NVIDIA Cloud Partner (NCP) infrastructure operations to join our DSX team. This role involves working closely with strategic NVIDIA Cloud Partners to build and improve the operational capabilities essential for running large-scale NVIDIA accelerated infrastructure reliably in production.

Your role involves guiding partners beyond the initial cluster deployment and validation phase into advanced Day 2 operations. These operations cover ongoing infrastructure health, observability, lifecycle management, quick remediation, performance validation, and operational readiness. You will engage directly with partner engineering and operations teams to develop consistent approaches that support NVIDIA workloads and the broader external customer environments of the partners. This is a highly technical, hands‑on role at the intersection of NVIDIA accelerated computing, cloud infrastructure, distributed systems, and production operations.

What you’ll be doing:

• Lead NCP Day 2 operational readiness efforts. Collaborate directly with NVIDIA Cloud Partners to set up the systems, procedures, automation, and operational methods necessary to consistently manage NVIDIA accelerated infrastructure following initial deployment and activation.

• Build continuous infrastructure validation. Develop and implement methods to continuously validate GPU, CPU, storage, and network health. Do this across large-scale AI clusters to identify degraded infrastructure before it impacts critical training or inference workloads.

• Establish observability and operational telemetry. Help NCPs implement comprehensive telemetry, monitoring, alerting, dashboards, and operational signals across compute, GPU, InfiniBand/RoCE networking, storage, Kubernetes, and AI workloads.

• Develop automated detection and remediation. Build workflows to detect, isolate, drain, repair, validate, and return unhealthy infrastructure to service while minimizing disruption to customer workloads.

• Refine fleet lifecycle administration. Implement scalable strategies for managing sizable GPU fleets, including NVIDIA driver and firmware lifecycle administration, Kubernetes node maintenance, OS patching, configuration management, upgrades, and configuration drift identification.

• Operationalize NVIDIA reference architectures. Translate NVIDIA NCP requirements and reference architectures into production operating practices, validation criteria, runbooks, automation, and measurable operational standards.

• Define operational health and readiness. Develop health signals, SLOs, important metrics, acceptance criteria, and ongoing validation mechanisms that provide NVIDIA and NCPs with clear insight into infrastructure reliability and service readiness.

• Build reusable operational frameworks. Develop tooling, automation, implementation guides, runbooks, operational playbooks, and reference implementations that can be applied consistently across multiple NCP environments.

What we need to see:

• 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 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 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.

Ways to stand out from the crowd:

• Experience managing extensive GPU or accelerated computing infrastructure that supports AI training and inference workloads.

• Experience with NVIDIA technologies including DGX/HGX systems, CUDA, NVLink/NVSwitch, NVIDIA networking, InfiniBand, RoCE, GPU Operator, Network Operator, or related NVIDIA infrastructure software.

• Proven experience collaborating with NVIDIA Cloud Partners, hyperscale cloud providers, managed AI clouds, or extensive service-provider infrastructure and operating SLOs for large-scale compute infrastructure and using operational data to improve availability, performance, and fleet efficiency.

• Extensive knowledge of infrastructure observability tools including Prometheus, Grafana, OpenTelemetry, Alertmanager, and scalable telemetry pipelines and translating reference architectures or infrastructure requirements into repeatable production operating models across multiple customer or partner environments.

• Knowledge of failure modes related to large distributed AI workloads and the infrastructure features necessary to consistently support extended training and production inference.

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for phenomenal people like you to help us accelerate the next wave of artificial intelligence. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN for Level 4, and 375,000 PLN - 650,000 PLN for Level 5.