Head of Global Customer Engineering - Internal EDA Infrastructure

at Nvidia

📍 United States

USD 272,000-488,800 per year

SENIOR

✅ Remote

Tech Stack
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AI @ 3 Communication @ 9 GPU GenAI Generative AI @ 4 HPC Kubernetes @ 4 LLM @ 3 Leadership @ 9 Machine Learning Slurm @ 4

Details

Every NVIDIA GPU starts as a job on NVIDIA's internal EDA infrastructure. Thousands of hardware engineers designing next-generation silicon depend on this infrastructure being fast, available, and frictionless. NVIDIA is hiring a hands-on engineering leader to develop Global Customer Engineering for Internal EDA Infrastructure, the organization positioned between chip designers and the platforms they use.

This multi-team leadership role spans managers and technical leads across a globally distributed organization and has two co-equal mandates: building a world-class customer engineering organization for EDA infrastructure and leading the Kubernetes platform team that delivers one of its customer services end to end.

The role involves designing software solutions and automation pipelines to eliminate operational overhead. The leader is expected to remain connected to live production environments and engineer permanent automated remedies for recurring customer engineering toil.

Responsibilities

Build and develop the leaders managing a globally distributed team of customer-facing engineers responsible for the reactive support queue for EDA infrastructure.
Ensure escalations into service engineering contain clean, high-quality signal.
Lead the Kubernetes platform team as a first-class customer engineering service, owning availability, roadmap, developer experience, and operational excellence.
Own the evolution of the L0 AI support agent from an FAQ bot into a system that safely resolves real issues against internal systems and measurably reduces human support queue volume.
Define and defend service-level objectives for operational performance and secure stakeholder buy-in.
Drive deep support and continuous improvement across EDA job schedulers, including LSF and Slurm, Kubernetes platforms, and internal LLM harnesses used by chip designers.
Influence technical roadmaps by converting support trends into engineering requirements that ship.
Use automation and AI/ML to improve support efficiency and reduce ticket volume.

Requirements

12+ years of experience building and leading engineering support organizations spanning reactive and proactive customer engineering.
5+ years of experience leading a globally distributed organization, including developing managers and leaders.
Deep technical familiarity with Kubernetes, EDA workloads, job schedulers, and AI/LLM infrastructure foundations.
Exceptional communication skills, with credibility among individual engineers and executive leadership.
Bachelor's degree in a relevant engineering field or equivalent experience.

Preferred Qualifications

Experience leading a platform team and a customer-facing team simultaneously.
Experience architecting self-healing support ecosystems, including shift-left tooling that resolves issues before tickets are created.
Experience leading the full lifecycle of generative AI and LLM-based support assistants integrated deeply and safely with internal infrastructure.
Experience operating LSF, Slurm, or Kubernetes at a scale where manual intervention was a bottleneck, and leading automation to remove that bottleneck.