ML Ops Engineer

Luminary Cloud helps engineering companies be more competitive by getting to market faster, creating new, better products, and reducing development risk. We do this with our Physics AI platform, the fastest and easiest way to build and deploy models to understand and instantly predict physical reality with precision. Customers span industries from automotive and aerospace, to leading sporting equipment providers, including Otto Aviation, Joby Aviation, Piper Aircraft and Trek Bikes. Luminary is a Series B company and is headquartered in San Mateo, California.

MLOps Engineer | Luminary Cloud

Luminary Cloud is transforming how the world's most innovative companies generate vast amounts of CFD simulation data for Physics AI, design exploration, and optimization. Backed by Sutter Hill Ventures, our Series B startup is at the forefront of the transition to Physics-based AI through our scalable cloud platform.

Key Duties, Responsibilities, and Deliverables

• Build and maintain robust MLOps infrastructure enabling ML engineers and data scientists to train, track, and deploy models seamlessly without managing low-level Kubernetes infrastructure.
• Design and implement automated training pipelines and experiment tracking systems using modern MLOps frameworks including Kubeflow, MLflow, and Argo Workflows.
• Develop scalable data pipelines for large volumes of unstructured data, with particular focus on 3D geometric data using VTK and physics simulation outputs.
• Deploy machine learning models and set up production inference pipelines with focus on performance and reliability.
• Manage model registries and integrate them with automated workflows for seamless model lifecycle management.
• Implement comprehensive monitoring systems for continuous performance tracking of ML models in production environments.
• Collaborate with cross-functional teams to ensure MLOps infrastructure meets the evolving needs of physics-based AI applications.
• Write production-level code with velocity, maintaining high standards for performance and scalability.
• Optimize cloud infrastructure on Google Cloud Platform, leveraging Docker, Kubernetes, and Vertex AI for efficient resource utilization.

Expertise and Qualifications

Background and Experience