below marketmost similar roles pay hereabove market
This role pays more than
99%
of similar roles. Most pay
$205,750–$285,000
— the blue band above.
At the midpoint, this role pays about
$404k
versus about
$245k
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
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Distinguished Engineer, Production Engineering, Cluster Management joins the Production Engineering organization to serve as a senior technical leader for DGX Cloud GPU capacity. This role involves defining the long-range technical strategy and architectural direction for cluster operations across on-prem, hyperscalers, and NVIDIA Cloud Partner environments. You will build durable workflows, APIs, and automation frameworks to ensure large-scale production systems remain reliable, manageable, and scalable. Key responsibilities include managing Kubernetes service management, hardware readiness, and service reliability collaborations to maintain infrastructure health. The position requires deep expertise in distributed systems, Linux, networking, and containers, utilizing languages like Python and Go. You will solve complex operational problems by establishing standards for runtime delivery, restoration, and release safety across heterogeneous infrastructure environments.
Define the long-range technical strategy for operating DGX Cloud clusters across local data centers, hyperscalers, and NeoCloud environments.
Establish architectural direction and operating standards for cluster lifecycle, runtime delivery, restoration, and release readiness.
Build durable workflows, interfaces, and engineering handshakes across Kubernetes production services, hardware preparation, and bare-metal systems.
Develop automation, APIs, and readiness gates to move new capacity into stable production while maintaining existing capacity balance.
Implement production operating approaches that reduce manual input and increase consistency, traceability, and release safety.
Identify operational friction and convert it into durable improvements in software, processes, and operational interfaces.
Lead architecture reviews and set technical standards to improve operability, resilience, scalability, and performance across 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.
Deep experience with Kubernetes-based production systems, infrastructure automation, or distributed systems operations.
Strong software engineering skills in languages such as Python, Go, or similar low-level programming languages.
Deep understanding of distributed systems, Linux, networking, containers, and production reliability concerns.
Strong architectural judgment and a history of simplifying complex operational problems through reusable software and technical strategy.
Defined the structural foundation for a large, heterogeneous infrastructure environment spanning multiple platforms or providers (preferred).
AWS
Microsoft Azure
Google Cloud
Java
Python
Go
JavaScript
TypeScript
Swift
Machine Learning
Platform Engineering
Enterprise Architecture
Observability
Cloud Computing