Senior Production Engineer – DGX Cloud

NVIDIA

. Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments .

Posted 10/2/2026full-timeRemote • California • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and operating production services and large-scale distributed systems, with a strong focus on automation, reliability, and incident response. Proficient in using infrastructure as code and GitOps methodologies to enhance service deployment and management.

Highest-signal resume keywords

Production Services ManagementInfrastructure As CodeStrong Programming Skills In PythonSRE Principles UnderstandingAutomation For Service Deployments

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Applicant Tracking System Keywords

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Hard Skills

PythonGoLinuxKubernetesCloud InfrastructureDistributed SystemsNetworking FundamentalsAutomation ToolsConfiguration ManagementService Instrumentation

Soft Skills

Clear Technical CommunicationCollaboration Across Teams

Tools & Technologies

NVIDIA Cloud FunctionsSGLangVLLMNVIDIA DynamoGitOps

Certifications & Qualifications

BS/MS In Computer Science

Industry Keywords

Control Plane ServicesModel DeploymentsInference WorkloadsService HealthSLIsSLOsError BudgetsIncident Response

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesLinuxPythonGo

About the role

Key responsibilities & impact

Build and operate production software, automation, and tooling for control plane services, model deployments, and inference and agentic workloads across DGX Cloud environments
Improve the reliability of inference and agentic platforms and services, including NVIDIA Cloud Functions, SGLang- and vLLM-based endpoints, and inference services built with NVIDIA Dynamo
Improve endpoint availability, inference routing, capacity management, and service health
Use infrastructure as code and GitOps to deploy, configure, validate, upgrade, and recover services consistently across environments
Build workflows for service enablement, model releases, handoff, deprecation, and ongoing operations
Define and instrument SLIs and SLOs for inference and control plane services and use error budgets to guide reliability improvements
Participate in on-call and incident response, troubleshoot failures, and turn recurring issues into automation and durable fixes
Collaborate with model, platform, storage, networking, security, and GPU infrastructure teams to design and operate services safely at scale

Requirements

What you’ll need

8+ years of experience building or operating production services and large-scale distributed systems, including hands-on automation
Strong programming skills in Python, Go, or a comparable language
Experience developing tools for production operations
Experience with infrastructure as code, configuration management, or GitOps
Experience building automation for repeatable service deployments and changes
Strong knowledge of Linux, Kubernetes, containers, cloud infrastructure, distributed systems, and networking fundamentals
Ability to diagnose failures in production
Understanding of SRE principles, including SLIs, SLOs, error budgets, incident response, and reducing operational toil
Experience instrumenting services and using metrics, logs, and traces to understand system behavior and improve reliability
Clear technical communication and ability to work across engineering teams
BS/MS in Computer Science or equivalent experience

Benefits

Comp & perks

Equity
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