Jobs at Norbert Health

Lead development and deployment of real-time computer vision models for edge robots: model design, optimization (quantization/pruning/TensorRT), MLOps pipelines, video and signal processing for contactless vitals, and productionizing models on embedded hardware.

As a Senior Backend Engineer, you'll architect and improve the backend system, manage cloud infrastructure, design APIs, ensure compliance, and guide critical architecture decisions.

As an Applied AI Engineer, you'll automate MLOps pipelines using ML components and foundation models, ensuring deployment across robots in healthcare settings with rigorous performance measures and regulatory compliance.

Develop automated, production-grade MLOps pipelines for AI in healthcare, focusing on integrating foundation models and real-time streaming systems under regulatory constraints.

The Applied AI Engineer will develop automated MLOps pipelines for integrating and deploying AI models in healthcare robotics, ensuring compliance with regulations and achieving clinical accuracy targets.

The Senior Backend Engineer will architect and implement a unified backend system, manage AWS cloud infrastructure, design APIs, ensure compliance, and lead critical architectural decisions.

The Signal Processing Engineer will design and implement algorithms to extract vital signs from video, validate algorithms, and integrate with computer vision systems while ensuring clinical-grade accuracy.

Develop and validate algorithms for extracting physiological signals from video data, ensuring clinical-grade accuracy, and integrate with computer vision systems.

Develop and validate algorithms for extracting clinical-grade physiological signals from video data. Collaborate with computer vision engineers and build validation pipelines.

Lead the development of deep learning sensing technology by designing and deploying computer vision models for real-time inference, optimizing for edge deployment, and establishing MLOps pipelines.

Lead the development of computer vision models for healthcare robots, optimizing them for edge deployment and managing MLOps pipelines.

The Embedded Linux Engineer will design and maintain the embedded platform, develop toolchains and collaborate on optimizing algorithms, focusing on automation and hardware integration.