ML Ops DSX Senior Engineer
$184k - $357k • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
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. In this role, you will develop innovative solutions that advance AI infrastructure capabilities and directly influence customer success with breakthrough AI initiatives.
Responsibilities
- Build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines.
- Act as the main technical contact for strategic NCPs, offering remote and on-site support, troubleshooting complex production problems, and guiding partner engineering teams.
- Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling systems.
- Profile and tune large-scale training and inference workloads on NCP platforms, implementing observability and SLO/SLA monitoring to reduce latency, cost, and operational risk.
- Implement and expand NVIDIA reference architectures on partner platforms, developing integrations with partner control planes and customer environments.
- Build detailed implementation guides, runbooks, and post-mortem documentation for running NVIDIA AI workloads at scale on NCP platforms.
Requirements
- BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or 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.
- Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling.
- Demonstrated AI/ML experience supporting large-scale training and inference workloads in production environments.
- Solid programming skills in Python/Go, with hands-on experience using frameworks such as PyTorch or TensorFlow.
- Demonstrated capability to collaborate with customer and partner engineering teams, guide technical investigations, and bring issues to resolution.
- Excellent communication and technical presentation skills.
Benefits
- Competitive salaries
- Generous benefits package
- Equity
- Benefits