ML Ops DSX Senior Engineer

NVIDIA•Santa Clara, CA

1d•$184,000 - $356,500

About The Position

NVIDIA is seeking an NCX Senior Engineer to join its DSX team and collaborate with strategic customers to implement and enhance AI workloads. The role provides hands-on technical assistance for advanced AI deployments and distributed systems, helping customers achieve efficient performance from NVIDIA’s AI platform across varied environments. The engineer will develop solutions that advance AI infrastructure capabilities and directly influence customer success with AI initiatives. The position involves building and deploying custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines based on NVIDIA reference architectures. It serves as the main technical contact for strategic NCPs, providing remote and on-site support, troubleshooting production problems, and guiding partner engineering teams on NVIDIA platform guidelines. The engineer also deploys and manages workloads across DGX Cloud, NCP data centers, and major CSP environments, and profiles and tunes large-scale training and inference workloads. Additional work includes implementing observability and SLO/SLA monitoring; reducing latency, cost, and operational risk; expanding NVIDIA reference architectures on partner platforms; integrating with partner control planes and customer environments; ensuring connectivity across APIs, data pipelines, and enterprise software; and producing implementation guides, runbooks, and post-mortem documentation for operating NVIDIA AI workloads at scale on NCP platforms.

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, such as LLMs, generative models, or recommendation systems, in production or critically important environments.
Solid programming skills in Python or Go, with hands-on experience using frameworks such as PyTorch or TensorFlow for training and serving.
Ability 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, including the ability to articulate architectures, trade-offs, and recommendations to engineering and leadership audiences.

Nice To Haves

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 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.
Background in infrastructure as code, such as Terraform, Ansible, or similar tools, for repeatable deployment and configuration of GPU-accelerated clusters and NCP building blocks.

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 main technical contact for strategic NCPs, provide 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, and lead efforts to reduce latency, cost, and operational risk.
Implement and expand NVIDIA reference architectures on partner platforms and develop integrations with partner control planes and customer environments.
Ensure smooth connectivity among APIs, data pipelines, and enterprise software.
Create implementation guides, runbooks, and post-mortem documentation that codify standard methodologies for running NVIDIA AI workloads at scale on NCP platforms.