Senior AI Infrastructure Engineer - EDA Infrastructure

NVIDIA•Durham, NC

9d•$184,000 - $356,500

About The Position

AI Infrastructure Engineers at NVIDIA build the systems, tooling, and data infrastructure that enable operation of NVIDIA’s GPU cloud services. This role supports engineering teams in innovating while proactively identifying, tracking, and mitigating risks across technical tasks. It is suited to engineers working across product, infrastructure, and software engineering who want to build automated, intelligence-driven systems for NVIDIA’s AI platforms. The role covers telemetry, operational excellence, and cataloging and inventory. It includes building scalable telemetry pipelines and consistent instrumentation, collection, storage, and access patterns; delivering dashboards, alerting, and analysis capabilities; standardizing and automating incident, maintenance, on-call, and support workflows; integrating operational data and lifecycle signals; developing reporting and AI-assisted tooling; and maintaining trusted infrastructure catalogs and self-service discovery capabilities.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
8+ years of experience in infrastructure platform engineering or tooling.
Proficiency in one or more programming languages such as Python, Go, TypeScript, or Java.
Strong understanding of software and infrastructure principles, with experience applying them in production environments.
Ability to lead cross-functional initiatives spanning internal teams and external partners across engineering, product, finance, and security.

Nice To Haves

Experience building and operating incident-management processes with internally built and/or externally vended SaaS tools.
Experience building, deploying, and maintaining ML models in production systems, along with familiarity with AI agent frameworks or orchestration tools.
Experience building and operating modern observability platforms for scalable metrics, logs, traces, and profiling.
Experience working with service catalogs and configuration management databases (CMDB).

Responsibilities

Build and operate scalable telemetry pipelines for metrics, logs, traces, and events across on-premise, CSP, and NCP clusters.
Establish common instrumentation, collection, storage, and access patterns for consistent telemetry generation and consumption.
Deliver dashboards, alerting, and analysis capabilities that improve service visibility, detection, and troubleshooting.
Standardize and automate incident, maintenance, service on-call, and support on-call workflows across HWInf.
Integrate operational data and lifecycle signals to improve ownership, escalation, communication, and post-incident learning.
Build reporting and AI-assisted tooling that reduces manual toil and improves operational responsiveness.
Build and maintain physical hardware and software catalogs as trusted sources of truth for infrastructure inventory, service ownership, dependencies, and documentation.
Create consistent data models and integration pipelines connecting clusters, hardware, services, teams, and operational workflows.
Provide self-service discovery capabilities so engineers can identify what they operate, who owns it, and how to support it.