Principal Software Engineer, DGX Cloud Production Engineering
at Nvidia
USD 272,000-431,200 per year
Used Tools & Technologies
Machine LearningRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 6
Go @ 7
Kubernetes @ 4
Linux @ 7
Python @ 7
Distributed Systems @ 8
Networking @ 6
API @ 4
GPU @ 4
Observability @ 4
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA DGX Cloud is scaling GPU infrastructure across internal, partner, and cloud environments. This role is for senior technical leaders who can define architecture, lead through influence, build critical systems, and turn ambiguous infrastructure problems into durable software and operating models.
Responsibilities
- Define and execute the technical strategy for DGX Cloud cluster operations, building the automation, GitOps, and Day 2 reliability needed to operate large-scale GPU clusters across NVIDIA Cloud Partners (NCPs) and on-prem environments.
- Lead design and implementation of systems for cluster lifecycle, validation, repair, upgrades, observability, and readiness.
- Establish patterns for Kubernetes-based GPU cluster operations across partner and on-prem environments.
- Identify and eliminate operational toil through software, APIs, automation, and agent-assisted workflows.
- Set technical standards for production readiness, SLOs, incident response, handoff gates, and operational acceptance.
- Mentor engineers and influence platform, infrastructure, storage, networking, security, and workload teams.
Requirements
- 15+ years of experience building and operating large-scale distributed systems or cloud infrastructure.
- Deep experience with Kubernetes, Linux, infrastructure automation, and production operations.
- Strong programming experience in Go, Python, or similar languages.
- Proven ability to lead complex cross-organization technical initiatives.
- Experience designing reliable systems with clear SLOs, observability, incident response, and automation.
- BS/MS in Computer Science or equivalent experience.
Ways to stand out
- Experience with GPU clusters, AI/ML infrastructure, Kubernetes operators, GitOps, BMaaS/VMaaS, managed Kubernetes, or multi-cloud fleet operations.
- Experience building internal platforms, control planes, lifecycle automation, or production readiness frameworks.
- Track record of turning operational pain into reusable software, APIs, and engineering standards.
Compensation & Other Details
- Base salary range: 272,000 USD - 431,250 USD (final base salary will be determined based on your location, experience, and pay of employees in similar positions).
- You will also be eligible for equity and benefits.
- Applications for this job will be accepted at least until May 22, 2026.
- NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.