As an NCX Engineer, AI Accelerator at NVIDIA, you will join the AI Accelerator team as a senior-level professional, collaborating with strategic customers to implement and enhance advanced AI workloads. Your daily tasks include building custom AI solutions on NCP and Neo Cloud platforms, offering technical support for complex production issues, and deploying AI workloads across various environments using Kubernetes and GPU scheduling systems. You will also profile large-scale training and inference workloads, develop integrations with partner control planes, and create detailed implementation guides. The role requires expertise in Linux, distributed computing, Kubernetes, and GPU scheduling, along with strong programming skills in Python or Go and experience with AI/ML frameworks like PyTorch or TensorFlow. Familiarity with NVIDIA’s ecosystem, including DGX systems and CUDA, is essential, as well as deep knowledge of MLOps practices and cloud-native technologies such as Prometheus and Grafana.
Skills
Kubernetes
Python
Go
Terraform
Prometheus
Grafana
PyTorch
TensorFlow
Docker
CI/CD
MLOps
OpenTelemetry
GitOps
Linux
CUDA
NVIDIA_NeMo
NVIDIA_Triton
NVIDIA_InfiniBand
NVIDIA_RoCE
Salesforce
ServiceNow
What you'll do
Build custom AI solutions on NCP and Neo Cloud platforms for distributed training and inference.
Provide remote and on-site technical support to strategic customers for complex production issues.
Deploy and manage AI workloads across DGX Cloud, data centers, and CSP environments using Kubernetes.
Profile and tune large-scale AI workloads to reduce latency, cost, and operational risk.
Develop integrations with partner control planes and ensure API connectivity in customer environments.
What we're looking for
8+ years experience in customer-facing technical roles like Solutions Engineering or ML Infrastructure Engineering.
Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling.
Demonstrated AI/ML experience supporting large-scale training and inference workloads in production environments.
Solid programming skills in Python/Go with hands-on experience using PyTorch or TensorFlow for training and serving.
Experience collaborating with customer and partner engineering teams to guide technical investigations and resolve issues.
Deep familiarity with MLOps practices including containerization, CI/CD pipelines, observability stacks, and GitOps workflows.
Market check
Salary context
This
$184,000–$287,500
range sits above
71%
of similar postings on FindRole.
Peer median band
$166,500–$246,300
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$162,000–$246,150
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
801 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 797 roles with salary data.