Full-Time

NCX Engineer

AI Accelerator

Posted on 7/4/2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$184k - $356.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Seattle, WA, USA + 1 more

More locations: Santa Clara, CA, USA

Hybrid

Hybrid role with on-site visits to customer locations in CA or WA.

Bachelor's, Master's, PhD

Category
AI & Machine Learning (1)
Required Skills
Kubernetes
MLOps
Python
TensorFlow
PyTorch
Machine Learning
Docker
Go
Observability
REST APIs
Linux/Unix

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Requirements
  • BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
  • 8+ years of experience in customer facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally supporting large‑scale cloud or service provider environments.
  • Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
  • Demonstrated AI/ML experience supporting large‑scale training and inference workloads (e.g., LLMs, generative models, recommendation systems) in production or critically important environments.
  • Solid programming skills in Python/Go, with hands‑on experience using frameworks such as PyTorch or TensorFlow for training and serving.
  • Demonstrated capability to collaborate with customer and partner engineering teams in fast-paced environments, guide intricate technical investigations, and bring issues to root cause and resolution.
  • Excellent communication and technical presentation skills, with the ability to clearly articulate architectures, trade‑offs, and recommendations to both engineering and leadership audiences.
Responsibilities
  • Build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines constructed on NVIDIA reference architectures.
  • Act as the main technical contact for strategic NCPs, offer remote and on-site support, troubleshoot complex production problems, and guide partner engineering teams on NVIDIA platform guidelines.
  • 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 detailed efforts to reduce latency, cost, and operational risk.
  • Implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments, and ensure smooth API, data pipeline, and enterprise software connectivity.
  • Build detailed implementation guides, runbooks, and post‑mortem documentation that codify standard methodologies for running NVIDIA AI workloads at scale on NCP platforms.
Desired Qualifications
  • Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, and NVIDIA networking technologies such as InfiniBand and RoCE.
  • Direct experience collaborating with NVIDIA Cloud Partners, hyperscale CSPs, or managed AI cloud platforms, including implementation of NVIDIA reference architectures for AI infrastructure.
  • Deep familiarity with MLOps and cloud‑native practices: containerization, CI/CD pipelines, observability stacks (Prometheus, Grafana, OpenTelemetry), and GitOps workflows.
  • Background in infrastructure as code (Terraform, Ansible, or similar) for repeatable deployment and configuration of GPU‑accelerated clusters and NCP building blocks.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • Amazon ordered 2 million GPUs for 2027-2028, validating hyperscaler demand.
  • August 2026 Lancium investment secured 4 GW leased capacity and 15 GW pipeline.
  • Blackwell Ultra and Vera Rubin shipments began; production ships in late 2026.

What critics are saying

  • Taiwan prosecutors indicted NVIDIA staff August 24, 2026 over illegal China server exports.
  • China’s antitrust probe still threatens fines, remedies, and slower mainland sales.
  • China export controls and ASIC substitution can cut NVIDIA off from a trillion-dollar market.

What makes NVIDIA unique

  • CUDA and NVLink lock developers into NVIDIA’s full-stack AI platform.
  • FY2026 revenue hit $215.9 billion, with data center revenue $193.7 billion.
  • Rubin launched February 2026, targeting 10x lower inference costs than Blackwell.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-1%

2 year growth

-2%
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