Senior Production Engineer — DGX Cloud Infra & Automation

NVIDIA Corporation; Santa Clara (CA); Vor Ort; USD 224.000 - 357.000; Vollzeit

Zusammenfassung

NVIDIA Corporation is seeking a Senior Production Engineer to build software and automation for DGX Cloud control planes, model deployments, and inference workloads. You will improve service reliability and operate across Kubernetes clusters in multiple clouds, with a focus on health, observability, and safe rollouts.

Expect to collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to scale operations, define SLIs/SLOs, and reduce toil through automation.

Qualifikationen

• 8+ years of experience building or operating production services and large-scale distributed systems.
• Strong programming in Python or Go for production tools.
• Experience with infrastructure as code and GitOps for deployments.
• Strong knowledge of Linux, Kubernetes, containers, cloud infra, networking.
• Understanding of SRE principles and incident response.
• Experience instrumenting services with metrics, logs, traces.
• Clear technical communication across engineering teams.

Aufgaben

• Build and operate production software, automation, and tooling for control plane services.
• Improve reliability of inference and agentic platforms across DGX Cloud environments.
• Define and instrument SLIs and SLOs, error budgets, and health visibility.
• Participate in on-call and incident response, troubleshoot failures across routing, runtimes, and hardware.
• Collaborate with model, platform, storage, networking, security, and GPU infra teams to operate safely at scale.

Kenntnisse

Python

Go

Automation for production ops

Linux

Kubernetes

Cloud infrastructure

SRE principles

Ausbildung

BS/MS in Computer Science

Tools

Terraform

Argo CD

CI/CD

Jobbeschreibung

NVIDIA Corporation is seeking a Senior Production Engineer to build software and automation for DGX Cloud control planes, model deployments, and inference workloads. You will improve service reliability and operate across Kubernetes clusters in multiple clouds, with a focus on health, observability, and safe rollouts.

Expect to collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to scale operations, define SLIs/SLOs, and reduce toil through automation.