NCX Engineer, AI Accelerator

Apply for the NCX Engineer, AI Accelerator position at NVIDIA. As an NCX Engineer, you will develop innovative solutions that advance AI infrastructure capabilities. You will directly influence customer success with breakthrough AI initiatives.

Key responsibilities include:

• Building and deploying custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines constructed on NVIDIA reference architectures.
• Acting as the main technical contact for strategic NCPs, offering remote and on-site support, troubleshooting complex production problems, and guiding partner engineering teams on NVIDIA platform guidelines.
• Deploying and managing AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling systems aligned to NCP builds.
• Profiling and tuning large-scale training and inference workloads on NCP platforms. Implementing observability and SLO/SLA monitoring. Leading detailed efforts to reduce latency, cost, and operational risk.
• Implementing and expanding NVIDIA reference architectures on partner platforms, developing integrations with partner control planes and customer environments, and ensuring smooth API, data pipeline, and enterprise software connectivity.
• Building detailed implementation guides, runbooks, and post-mortem documentation that codify standard methodologies for running NVIDIA AI workloads at scale on NCP platforms.

Requirements include:

• A Bachelor's degree 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.

Preferred qualifications include experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, and NVIDIA networking technologies such as InfiniBand and RoCE.