Principal Software Engineer, Distributed Systems Engineer – DGX Cloud

NVIDIA

. Contribute to the DGX Cloud team responsible for production systems enabling large-scale GPU clusters for AI workloads .

Posted 9/30/2026full-timeRemote • North Carolina • United StatesLead💰 $272,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive experience in software engineering with a focus on Kubernetes for managing large-scale GPU clusters, ensuring reliability and performance through effective monitoring and incident management. Proficient in systems programming languages such as Go or Python, with a solid understanding of data structures and algorithms.

Highest-signal resume keywords

Kubernetes Cluster OperationsGPU Resource SchedulingSoftware Development with Kubernetes APIsSystems Programming in Go or PythonOperational Excellence in AI Infrastructure

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Software EngineeringKubernetesCluster Management SystemsData StructuresAlgorithmsIncident ManagementMonitoring and Health ManagementDistributed Systems AutomationGPU Hardware DiagnosticsTelemetry Analysis

Soft Skills

Strong Communication SkillsCollaboration with Multifunctional Teams

Tools & Technologies

Kubernetes APIsSlurmBright Cluster Manager

Certifications & Qualifications

BS in Computer ScienceBS in EngineeringBS in PhysicsBS in Mathematics

Industry Keywords

AI WorkloadsLarge-Scale Production SystemsTechnical OrganizationOperational Excellence

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesNode.jsPythonGo

About the role

Key responsibilities & impact

Contribute to the DGX Cloud team responsible for production systems enabling large-scale GPU clusters for AI workloads
Develop custom software for scheduling GPU resources on Kubernetes
Implement monitoring and health-management capabilities for reliability, availability, and scalability of GPU assets
Harness data streams from GPU hardware diagnostics, cluster telemetry, and network telemetry
Collaborate with teams across NVIDIA to ensure production AI clusters run reliably, consistently, and with maximum performance
Evaluate system failures and improve services through a defined incident-management process

Requirements

What you’ll need

Significant software engineering experience with Kubernetes, including cluster operations, operator development, node health monitoring, and GPU resource scheduling
Direct experience in a software engineering role within a highly technical organization, with demonstrable impact
Software development experience with Kubernetes APIs and frameworks, not just operating a cluster
Strong communication skills and ability to work with multifunctional teams, principals, and architects across organizational boundaries and geographies
15+ years in a similar role and experience with large-scale production systems
Experience with common software engineering principles, tools, and techniques
BS in Computer Science, Engineering, Physics, Mathematics, or a comparable degree, or equivalent experience
Systems programming language proficiency, including Go or Python
Solid understanding of data structures and algorithms
Technical competency managing and automating large-scale distributed systems independent of cloud providers
Advanced hands-on experience and deep understanding of cluster management systems such as Kubernetes, Slurm, or Bright Cluster Manager
Proven operational excellence maintaining reliable and performant AI infrastructure

Benefits

Comp & perks

Equity
Benefits 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score