Job description

NVIDIA is seeking a Distinguished Engineer to serve as a senior technical leader in the Production Engineering organization. The person will be passionate about leading cluster activities within DGX Cloud GPU capacity. Production Engineering at NVIDIA is tasked with ensuring large-scale production systems remain reliable, manageable, and progressively automated across DGX Cloud resources. Our method combines software engineering, systems engineering, and production expertise. This allows us to develop platforms, workflows, and operating models that preserve GPU infrastructure health, scalability, and availability for researchers and customers. This position focuses on the operational structure for DGX Cloud clusters across on-prem, hyperscalers, and NVIDIA Cloud Partner environments. The scope includes the engineering connections needed to ensure DGX Cloud capacity is fully functional in production: Kubernetes service management, provider and hardware readiness, on-prem infrastructure handling, deployment and operational preparedness, service reliability collaborations, and the processes that integrate these areas into a unified production system. This is a hands-on Distinguished Engineer role for a deeply technical leader who will define architectural direction for cluster operations throughout DGX Cloud. The right person will combine software engineering rigor, systems depth, and production judgment. They will set technical strategy and establish operating standards. They will guide the framework’s evolution for production operations. They will drive progress on cross-organizational capabilities to keep DGX Cloud capacity usable, supportable, and improving at scale. This role requires both the ability to go deep in building and implementation and the ability to lead through influence across multiple teams and high-consequence production outcomes. What you'll be doing: • Define the long-range technical strategy for operating DGX Cloud clusters consistently across local data centers, hyperscalers, and NeoCloud environments • Define the architectural direction and fundamental operating standards for cluster lifecycle, runtime delivery, restoration, release readiness, and steady-state operability concerning DGX Cloud capacity • Guide the roadmap and execution of critical cross-organizational investments that improve production readiness, operational safety, performance, and cross-team coordination • Make and influence high-impact technical decisions that build how platform, hardware, provider, and service teams work together to operate DGX Cloud resources in production • Build durable workflows, interfaces, and engineering handshakes across Kubernetes production service, provider and hardware preparation, on-prem and bare-metal • Restructure operations, and service-layer reliability domains • Build and evolve the automation, APIs, operating workflows, and readiness gates required to move new capacity into stable production and keep existing capacity balanced • Implement production operating approaches that lower manual input, establish clear ownership responsibilities, and increase consistency, traceability, and release safety within DGX Cloud environments • Partner closely with platform teams, hardware and provider engineering, service owners, and other Production Engineering leaders to identify repeated friction and convert it into durable improvements in software, processes, and operational interfaces • Raise the engineering bar for operability, resilience, scalability, and performance across cluster operations through build leadership, architecture review, and technical standards What we need to see: • 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 and organizations • Consistent record leading large, cross-team technical efforts from concept through production, including aligning collaborators, navigating for clarity and delivering measurable outcomes • Deep experience with one or more of these areas: 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 • Experience crafting operational workflows, APIs, service interfaces, or automation frameworks that become the standard way teams run production systems • Strong architectural judgment and a validated history of simplifying complex operational problems through reusable software, clear technical strategy, and durable engineering direction Ways to stand out from the crowd: • Defined the structural foundation for a large, heterogeneous infrastructure environment spanning multiple platforms or providers • Established widely used operating standards, architectures, APIs, or workflows that improved reliability, operability, or performance at company scale • Built automation and engineering interfaces that connect platform teams, infrastructure teams, and service owners into a consistent production system • Experience improving production readiness, restoration, runtime safety, or release quality for large-scale infrastructure • Equally comfortable setting technical strategy, reviewing architecture at scale, writing code, and driving adoption across organizational boundaries This role is purposely assigned to the cross-domain production operating model for DGX Cloud capacity. It does not involve a shared platform-software function for typical automation services. Success is achieved by making sure the larger DGX Cloud cluster estate functions optimally in a live environment. The position calls for strong technical leadership, consistent workflows, clear limits, and productive cross-team engineering coordination. #LI-Hybrid Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 7, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Define the long-range technical strategy for operating DGX Cloud clusters consistently across local data centers, hyperscalers, and NeoCloud environments
Define the architectural direction and fundamental operating standards for cluster lifecycle, runtime delivery, restoration, release readiness, and steady-state operability concerning DGX Cloud capacity
Guide the roadmap and execution of critical cross-organizational investments that improve production readiness, operational safety, performance, and cross-team coordination
Make and influence high-impact technical decisions that build how platform, hardware, provider, and service teams work together to operate DGX Cloud resources in production
Build durable workflows, interfaces, and engineering handshakes across Kubernetes production service, provider and hardware preparation, on-prem and bare-metal
Restructure operations, and service-layer reliability domains
Build and evolve the automation, APIs, operating workflows, and readiness gates required to move new capacity into stable production and keep existing capacity balanced
Implement production operating approaches that lower manual input, establish clear ownership responsibilities, and increase consistency, traceability, and release safety within DGX Cloud environments
Raise the engineering bar for operability, resilience, scalability, and performance across cluster operations through build leadership, architecture review, and technical standards
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 and organizations
Consistent record leading large, cross-team technical efforts from concept through production, including aligning collaborators, navigating for clarity and delivering measurable outcomes
Deep experience with one or more of these areas: 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
Experience crafting operational workflows, APIs, service interfaces, or automation frameworks that become the standard way teams run production systems
Strong architectural judgment and a validated history of simplifying complex operational problems through reusable software, clear technical strategy, and durable engineering direction
Defined the structural foundation for a large, heterogeneous infrastructure environment spanning multiple platforms or providers
Established widely used operating standards, architectures, APIs, or workflows that improved reliability, operability, or performance at company scale
Built automation and engineering interfaces that connect platform teams, infrastructure teams, and service owners into a consistent production system
Experience improving production readiness, restoration, runtime safety, or release quality for large-scale infrastructure
Equally comfortable setting technical strategy, reviewing architecture at scale, writing code, and driving adoption across organizational boundaries

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

kubernetesgopythonai

Platform: eightfold
Effort: medium