Senior Architect, DGX Cloud Cluster Production

Nvidia Corporation in; Santa Clara (CA); On-site; USD 320,000 - 489,000; Full time

Job summary

NVIDIA is seeking a Distinguished Engineer to lead production engineering for DGX Cloud cluster operations across on‑prem, hyperscalers, and partner environments. This hands‑on role blends software, systems, and production expertise to shape architecture, standards, and scalable workflows.

Lead cross‑functional programs to improve reliability, automate capacity, and balance demand while mentoring teams and defining operating models for large‑scale GPU infrastructure.

Qualifications

• 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 Kubernetes-based production systems, infrastructure automation, or distributed systems operations.
• Strong software engineering skills in 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.

Responsibilities

• 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.

Skills

Python

Go

Linux

Distributed systems

Production engineering

Architectural direction

Leadership

Education

Computer Science or related field

Tools

Kubernetes

Job description

NVIDIA is seeking a Distinguished Engineer to lead production engineering for DGX Cloud cluster operations across on‑prem, hyperscalers, and partner environments. This hands‑on role blends software, systems, and production expertise to shape architecture, standards, and scalable workflows.

Lead cross‑functional programs to improve reliability, automate capacity, and balance demand while mentoring teams and defining operating models for large‑scale GPU infrastructure.