This role pays more than
99%
of similar roles. Most pay
$186,825–$295,500
— the shaded band above.
At the midpoint, this role pays about
$404k
versus about
$241k
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, Cluster Management serves as a senior technical leader within the Production Engineering organization focusing on DGX Cloud GPU capacity. This role involves defining long-range technical strategies and architectural directions for cluster lifecycle management, runtime delivery, and steady-state operability across local data centers, hyperscalers, and NeoCloud environments. The individual will build durable workflows, APIs, and automation frameworks to ensure infrastructure health, scalability, and reliability while reducing manual intervention. Key responsibilities include establishing operating standards for Kubernetes production services, hardware readiness, and service-layer reliability. Required expertise includes extensive experience in distributed systems, Linux, networking, and containers, alongside proficiency in programming languages like Python or Go. The role addresses the complex challenge of integrating diverse infrastructure components into a unified, automated production system to ensure high-performance availability for large-scale GPU resources.
Define the long-range technical strategy for operating DGX Cloud clusters across local data centers and hyperscaler environments.
Establish architectural directions and operating standards for cluster lifecycle, runtime delivery, and steady-state operability.
Develop durable workflows, interfaces, and engineering handshakes between Kubernetes services, hardware providers, and bare-metal infrastructure.
Build and evolve automation, APIs, and readiness gates to move new capacity into stable production environments.
Implement operating models that reduce manual input while increasing consistency, traceability, and release safety.
Identify recurring operational friction points and convert them into durable improvements in software and process interfaces.
Lead cross-organizational investments to improve production readiness, performance, and reliability across the DGX Cloud estate.
Raise engineering standards for scalability and resilience through architecture reviews and technical leadership.
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.
Consistent track record of defining operating models, architectural direction, and engineering standards across multiple technical domains.
Experience leading large, cross-team technical efforts from concept through production while aligning collaborators and delivering measurable outcomes.
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.
AWS
Microsoft Azure
Google Cloud
Python
Java
Go
JavaScript
TypeScript
Swift
Machine Learning
Cloud Computing
Platform Engineering
Observability
Scalability