ML Ops DSX Senior Engineer

Details

NVIDIA is seeking an NCX Senior Engineer to join the DSX team and collaborate closely with strategic customers to implement and enhance advanced AI workloads. The role provides hands-on technical assistance for AI deployments and distributed systems, helping customers achieve efficient performance from NVIDIA's AI platform across varied environments.

Responsibilities

• Build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines based on NVIDIA reference architectures.
• Serve as the primary technical contact for strategic NVIDIA Cloud Partners, providing remote and on-site support, troubleshooting complex production problems, and guiding partner engineering teams on NVIDIA platform guidelines.
• Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major cloud service provider environments using Kubernetes, containers, and GPU scheduling systems.
• Profile and tune large-scale training and inference workloads on NCP platforms.
• Implement observability and SLO/SLA monitoring, and lead 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, ensuring connectivity across APIs, data pipelines, and enterprise software.
• Create implementation guides, runbooks, and post-mortem documentation for operating NVIDIA AI workloads at scale on NCP platforms.
• Collaborate with customer and partner engineering teams to investigate complex technical issues and drive them to root-cause resolution.

Requirements

• Bachelor's, master's, or doctoral degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent experience.
• At least 8 years of experience in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability Engineering, 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.
• Experience supporting large-scale AI/ML training and inference workloads, including LLMs, generative models, or recommendation systems, in production or other critically important environments.
• Strong programming skills in Python or Go.
• Hands-on experience with PyTorch or TensorFlow for training and serving.
• Excellent communication and technical presentation skills, with the ability to explain architectures, trade-offs, and recommendations to engineering and leadership audiences.

Preferred Qualifications

• Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, and RoCE.
• Experience working with NVIDIA Cloud Partners, hyperscale cloud service providers, or managed AI cloud platforms.
• Experience implementing NVIDIA reference architectures for AI infrastructure.
• Deep familiarity with MLOps and cloud-native practices, including containerization, CI/CD pipelines, observability stacks such as Prometheus, Grafana, and OpenTelemetry, and GitOps workflows.
• Experience with infrastructure as code, including Terraform, Ansible, or similar tools, for repeatable deployment and configuration of GPU-accelerated clusters and NCP building blocks.

Benefits

NVIDIA offers competitive salaries, equity, and a generous benefits package. The company is committed to fostering an inclusive work environment and is an equal opportunity employer.

Applications for this job will be accepted at least until November 1, 2026. This posting is for an existing vacancy.