Senior AI infrastructure engineer - EDA Infrastructure

NVIDIA•Westford, CA

About The Position

AI Infrastructure Engineers at NVIDIA build the systems, tooling, and data infrastructure that enable operation of our GPU cloud services. We are enabling engineering teams to innovate while proactively identifying, tracking, and mitigating risks across the entire technical task. This role is ideal for engineers who thrive at the intersection of product, infrastructure, and software engineering and who want to build automated, intelligence-driven systems that protect NVIDIA’s most critical AI platforms.

Requirements

BS degree in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
8+ years of experience in infrastructure security, platform engineering, or security 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 that span internal teams and external partners in varying disciplines 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 working with 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 to deliver scalable metrics, logs, traces, and profiling
Experience working with service catalog 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 so teams can generate and consume telemetry consistently.
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 that connect clusters, hardware, services, teams, and operational workflows.
Provide self-service discovery capabilities so engineers can quickly identify what they operate, who owns it, and how to support it.