Senior AI Infrastructure Engineer – Scale & Equity

Nvidia Corporation

Santa Clara (CA)

On-site

USD 184,000 - 356,500

Full time

14 days+

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Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking an NCX Senior Engineer to join our AI Accelerator team, partnering with strategic customers to implement and enhance groundbreaking AI workloads. You will deliver hands-on technical assistance for advanced AI deployments, distributed systems, and ensure customers realize efficient performance from NVIDIA's AI platform across varied environments.

You will lead deployments on NCP/Neo Cloud, optimize training and inference, and guide partner teams using Kubernetes, containers, and

Qualifications

BS/MS/PhD in CS/EE or related field, or equivalent experience.
8+ years in customer-facing technical roles such as Solutions Engineering, DevOps, SRE, or ML Infra, ideally in large cloud/service provider environments.
Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant platforms.
Demonstrated AI/ML experience supporting large-scale training and inference workloads (LLMs, generative models) in production.
Solid programming skills in Python/Go, with hands-on experience using PyTorch or TensorFlow.

Responsibilities

Build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines on NVIDIA reference architectures.
Act as main technical contact for strategic NCPs, provide remote and on-site support, troubleshoot complex production problems, guide partner engineering teams.
Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling systems aligned to NCP builds.
Profile and tune large-scale training and inference workloads on NCP platforms; implement observability and SLO/SLA monitoring; lead efforts to reduce latency, cost, and risk.
Implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments; ensure smooth API and data pipeline connectivity.
Build detailed implementation guides, runbooks, and post-mortem documentation codifying standard methodologies for NVIDIA AI workloads at scale on NCP platforms.