Senior Engineer

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Create innovative solutions that enhance AI infrastructure capabilities.

• Have a direct impact on customer success through pioneering AI initiatives.

• Design and implement custom AI solutions on NCP and Neo Cloud platforms, which include distributed training, inference optimization, and MLOps pipelines built on NVIDIA reference architectures.

• Serve as the primary technical liaison for strategic NCPs, providing both remote and on-site support, resolving complex production challenges, and advising partner engineering teams on NVIDIA platform protocols.

• Deploy and manage AI workloads across DGX Cloud, NCP data centers, and leading CSP environments using Kubernetes, containers, and GPU scheduling systems tailored to NCP builds.

• Profile and optimize large-scale training and inference workloads on NCP platforms.

• Implement observability and SLO/SLA monitoring.

• Lead comprehensive initiatives to minimize latency, cost, and operational risk.

• Expand and implement NVIDIA reference architectures on partner platforms, establish integrations with partner control planes and customer environments, and ensure seamless API, data pipeline, and enterprise software connectivity.

• Develop detailed implementation guides, runbooks, and post-mortem documentation that encapsulate best practices for executing 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.

• Over 8 years of experience in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, preferably in large-scale cloud or service provider environments.

• Strong proficiency in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service provider platforms.

• Proven AI/ML experience supporting large-scale training and inference workloads (e.g., LLMs, generative models, recommendation systems) in production or mission-critical settings.

• Solid programming abilities in Python/Go, with practical experience using frameworks such as PyTorch or TensorFlow for training and deployment.

• Demonstrated ability to work collaboratively with customer and partner engineering teams in dynamic environments, guiding complex technical investigations and resolving issues to their root causes.

• Exceptional communication and technical presentation skills, with the capability to clearly articulate architectures, trade-offs, and recommendations to both engineering and leadership audiences.

🏝️ Benefits

• Equity

• Benefits