Senior ML Ops Engineer

🔥 1 minute ago

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

💵 $184k - $356.5k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Develop innovative solutions that advance AI infrastructure capabilities • 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 • 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 • Use Kubernetes, containers, and GPU scheduling systems aligned to NCP builds • Profile and tune large-scale training and inference workloads • Implement observability and SLO/SLA monitoring • Reduce latency, cost, and operational risk • Implement and expand NVIDIA reference architectures on partner platforms • Develop integrations with partner control planes and customer environments • Ensure API, data pipeline, and enterprise software connectivity • Build implementation guides, runbooks, and post-mortem documentation • Codify standard methodologies for running NVIDIA AI workloads at scale

🎯 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 • Experience 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 in production or critically important environments • Solid programming skills in Python/Go • Hands-on experience using PyTorch or TensorFlow for training and serving • Ability to collaborate with customer and partner engineering teams, guide technical investigations, and resolve issues • Excellent communication and technical presentation skills • Experience with NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, and RoCE • Experience collaborating with NVIDIA Cloud Partners, hyperscale CSPs, or managed AI cloud platforms • Familiarity with MLOps and cloud-native practices, containerization, CI/CD, observability stacks, and GitOps workflows • Infrastructure-as-code experience with Terraform, Ansible, or similar tools

🏖️ Benefits

• Competitive salaries • Generous benefits package • Equity

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