Kubernetes Platform Delivery Manager for AI Workloads

NVIDIA; Washington; On-site; USD 224,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in Washington state seeks an Engineering Manager to lead the Customer Delivery and Self‑Service function for the DGX Cloud Kubernetes Platform and Production Engineering team. You will steer end‑to‑end delivery of production Kubernetes clusters for AI workloads, from request through enablement and handoff.

You’ll build and mentor a team, define roadmaps, automate workflows, and partner with platform, runtime, security, TPM, and infra groups to deliver scalable, self‑service solutions.

Qualifications

• 8+ years of industry experience, including 2+ years leading or managing engineers.
• Experience building platform APIs, self-service infrastructure, workflow automation, developer platforms, or customer onboarding systems.
• Strong understanding of Kubernetes, cloud infrastructure, distributed systems, or production engineering.
• Hands-on experience using AI coding tools and AI-enabled engineering workflows.

Responsibilities

• Lead a team of software and production engineers focused on Kubernetes customer delivery, onboarding, and self-service.
• Own end-to-end delivery of production Kubernetes clusters for AI workloads, from request through enablement, validation, and handoff.
• Drive a coordinated delivery plan with clear owners, dependencies, readiness gates, timelines, risks, and blockers.
• Partner across platform, runtime, release, fleet operations, CSE, product, TPM, security, and infrastructure teams.

Skills

Kubernetes & cloud infrastructure

Leadership & cross-functional

Workflow automation

AI-enabled engineering workflows

System integration

Education

BS or MS in Computer Science/Engineering

Tools

Terraform

GitOps

APIs & workflow engines

Job description

NVIDIA in Washington state seeks an Engineering Manager to lead the Customer Delivery and Self‑Service function for the DGX Cloud Kubernetes Platform and Production Engineering team. You will steer end‑to‑end delivery of production Kubernetes clusters for AI workloads, from request through enablement and handoff.

You’ll build and mentor a team, define roadmaps, automate workflows, and partner with platform, runtime, security, TPM, and infra groups to deliver scalable, self‑service solutions.