NVIDIA is seeking an NCX Senior Engineer to join our DSX team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads! You will deliver hands-on technical assistance for advanced AI deployments, intricate distributed systems, and ensure customers realize efficient performance from NVIDIA's AI platform across varied environments. We partner with the world's most innovative AI companies to address their most challenging technical problems.
What you will be doing:
In this role, you will develop innovative solutions that advance AI infrastructure capabilities. You will directly influence customer success with breakthrough AI initiatives.
- 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.
What we need to see:
- 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.
Ways to stand out from the crowd:
- 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 offers competitive salaries and a generous benefits package. It is recognized as one of the technology world’s most desirable employers. We have some of the most innovative and dedicated people working here. Due to rapid growth, our outstanding teams are expanding quickly. Join us to make a lasting impact on the world!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s, master’s, or Ph.D. in Computer Science, Computer Engineering, 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 Engineering, or ML Infrastructure Engineering
- Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling
- Experience supporting large-scale AI/ML training and inference workloads in production or mission-critical environments
- Strong programming skills in Python or Go
- Hands-on experience with PyTorch or TensorFlow for training and serving
- Ability to collaborate with customer and partner engineering teams, lead technical investigations, and resolve root causes
- Excellent communication and technical presentation skills
- Experience with NVIDIA DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, or RoCE
- Experience with NVIDIA Cloud Partners, hyperscale cloud service providers, or managed AI cloud platforms
- Familiarity with containerization, CI/CD, observability stacks, and GitOps workflows
- Infrastructure-as-code experience with Terraform, Ansible, or similar tools
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
