Senior ML Ops Engineer

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

📋 Description

• Create cutting-edge solutions to enhance AI infrastructure capabilities.

• Design and implement tailored AI solutions on NCP and Neo Cloud platforms.

• Execute distributed training, optimize inference, and establish MLOps pipelines using NVIDIA reference architectures.

• Serve as the primary technical liaison for key NVIDIA Cloud Partners.

• Deliver both remote and on-site support.

• Diagnose complex production issues and assist partner engineering teams.

• Deploy and oversee AI workloads across DGX Cloud, NCP data centers, and major CSP environments.

• Utilize Kubernetes, containers, and GPU scheduling systems in line with NCP builds.

• Analyze and optimize large-scale training and inference workloads.

• Implement observability and monitor SLO/SLA metrics.

• Minimize latency, costs, and operational risks.

• Expand and implement NVIDIA reference architectures on partner platforms.

• Create integrations with partner control planes and customer environments.

• Ensure connectivity for APIs, data pipelines, and enterprise software.

• Develop implementation guides, runbooks, and post-mortem documentation.

⛳️ Requirements

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical discipline, or equivalent experience.

• Over 8 years of experience in customer-facing technical positions such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering.

• Experience in supporting large-scale cloud or service provider ecosystems.

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

• Proven AI/ML experience in supporting large-scale training and inference workloads in production or mission-critical environments.

• Strong programming capabilities in Python or Go.

• Practical experience with PyTorch or TensorFlow for training and serving AI models.

• Ability to collaborate effectively with customer and partner engineering teams, lead technical investigations, and troubleshoot issues.

• Exceptional communication and technical presentation skills.

• Familiarity with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, and RoCE.

• Experience working with NVIDIA Cloud Partners, hyperscale CSPs, or managed AI cloud platforms.

• Understanding of MLOps and cloud-native methodologies, containerization, CI/CD, observability stacks, and GitOps workflows.

• Experience with Infrastructure-as-Code tools like Terraform, Ansible, or similar.

🏝️ Benefits

• Equity.

• Comprehensive benefits.

• Competitive salary.

• Generous benefits package.