ML Infrastructure Engineer

Laminar builds AI and sensing systems for fluid and process manufacturing. The ML Infrastructure Engineer develops infrastructure and tooling for researchers to train, evaluate, and deploy machine-learning models at scale, including deployment to factory-floor edge devices.

Responsibilities:

• Develop computer orchestration tooling for researchers to seamlessly launch modeling jobs on large-scale data – training, fine-tuning, inference
• Design model testing environments that automatically evaluate model performance without a human in the loop through semi-supervised metrics and process-aware priors
• Build model registries and automated deployment pipelines that support large-scale model tracking, versioning, and deployment on edge devices
• Develop monitoring tools for deployed models: detect model drift or anomalies, then trigger continuous training (CT) pipelines as needed
• Work with ML researchers, ML developers to design systems that meet their needs; work with software engineers to design systems that interact gracefully with existing infrastructure
• Build for our unique use-cases and problems – not for the average problem