Principal Platform Engineer, AI Inference & Global Scale

NVIDIA Corporation

Santa Clara (CA)

Hybrid

USD 248,000 - 391,000

Full time

14 days+

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Job summary

NVIDIA Corporation in Santa Clara, CA is seeking a Principal Software Engineer to lead the architectural vision for a massive global platform and to operationalize internal AI inference systems, spanning thousands of nodes and VMs.

You will collaborate with autonomous teams to create self‑service APIs and Terraform/OpenTofu providers, drive migrations of legacy workloads to modern Kubernetes, and shape capacity planning amid hardware constraints.

Qualifications

Bachelor’s degree in Engineering, Computer Science, Mathematics, or related field, or equivalent experience.
15+ years of proven experience in compute platform engineering, site reliability, or systems architecture with a heavy focus on automation at massive scale.
Deep expertise in Kubernetes architecture and designing/deploying virtualization architectures, specifically operating VMs inside K8s (KubeVirt, OpenShift).
In‑depth knowledge of hardware technologies (GPUs, high‑speed backplane networking) with a track record of mitigating hardware‑level failures, silent data corruption, and anomalies in large‑scale environments.
Experience running large global environments spanning bare metal, virtualized infrastructure, and cloud with a unified GitOps posture (ArgoCD or similar).
Proficiency in programming languages such as Go and/or Python, alongside expert‑level infrastructure‑as‑code development (Terraform, Config Management).
Strong leadership skills with the ability to influence technical direction across highly autonomous teams without relying on top‑down mandates.

Responsibilities

Define Platform Architecture: Lead initiatives to architect and transform our global enterprise compute platform—running thousands of nodes and tens of thousands of VMs and containers via OpenShift and KubeVirt—by defining service tiers, SLAs, and automated cluster lifecycles.
Operationalize Frontier AI Infrastructure: Build the operational foundation for our internal AI inference platform scaling to frontier‑class models. You will develop automated remediation pipelines, hardware watchdogs, and telemetry for pre‑release, rack‑scale GPU systems (including Blackwell and upcoming architectures).
Drive Strategic Capacity & Scale: Collect and review system data for capacity planning to navigate extreme hardware supply constraints. Develop proactive strategies, including public cloud bursting, hardware dogfooding, and evaluating alternative compute architectures (e.g., ARM).
Build the "Paved Road": Collaborate with highly autonomous NVIDIA engineering teams to drive cultural adoption of standard platforms. You will design compelling self‑service architectures, APIs, and Terraform/OpenTofu providers that teams want to use.
Lead Complex Migrations: Evaluate existing application architectures and drive the fraught but critical migration of massive legacy workloads—including large‑scale, long‑running VDI environments—into modern Kubernetes orchestration.