This role pays more than
99%
of similar roles. Most pay
$153,720–$266,659
— the shaded band above.
At the midpoint, this role pays about
$404k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Distinguished Engineer, Production Engineering, Data Center Automation serves as a senior technical leader within the Production Engineering group focused on cluster operations for DGX Cloud GPU capacity. This role involves defining long-range technical strategies and architectural visions for cluster lifecycles, runtime delivery, and steady-state operability across on-premises, hyperscaler, and NeoCloud environments. The individual will build robust workflows, interfaces, and engineering collaborations to ensure infrastructure health, scalability, and availability. Key responsibilities include managing Kubernetes services, coordinating release readiness, and overseeing bare-metal infrastructure operations. The role requires extensive expertise in distributed systems, infrastructure platforms, and production environments. By integrating software engineering and system knowledge, the candidate will establish operating standards and technical directions to solve complex problems regarding the reliability and performance of large-scale GPU infrastructure for researchers and customers across diverse cloud provider locations.
Define the long-range technical strategy for operating DGX Cloud clusters across on-premise and multi-cloud environments.
Establish the architectural vision and operational guidelines for cluster lifecycle, runtime delivery, and steady-state operability.
Guide cross-organizational investments to improve production readiness, operational safety, and performance of GPU infrastructure.
Make high-impact technical decisions to coordinate platform, hardware, provider, and service teams in production environments.
Develop robust workflows and interfaces for Kubernetes services, bare-metal operations, and service-layer reliability.
Set operating standards and engineering guidelines to ensure DGX Cloud resources remain scalable and maintainable.
Drive the evolution of the production model to automate and streamline large-scale cluster operations.
What we're looking for
BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field, or equivalent experience.
18+ years of experience building and operating large-scale distributed systems, infrastructure platforms, or production environments.
Confirmed company-level technical leadership at principal, distinguished, or equivalent scope in production engineering, SRE, infrastructure software, or cloud platforms.
Experience establishing operating models, architectural direction, and engineering standards across various technical domains and organizations.
Proven track record of leading large, cross-team technical efforts from concept through production to deliver measurable outcomes.
Expertise in Kubernetes service management and managing on-premise, bare-metal, and cloud infrastructure operations.
Experience developing automation and engineering interfaces that connect platform teams, infrastructure teams, and service owners.
Ability to define long-range technical strategy and architectural vision for cluster lifecycle and runtime delivery.