Riverside Research

Engineer

• Design, develop, and implement automated V&V and T&E frameworks to assess the accuracy, performance, and operational readiness of remote sensing data products, AI/ML models, and vendor-delivered geospatial capabilities
• Lead the technical evaluation of commercial and government remote sensing platforms, sensors (multispectral, hyperspectral, SAR, LiDAR), and associated data products against mission-specific requirements
• Develop and maintain scalable, production-grade machine learning pipelines for geospatial applications including change detection, land cover classification, object detection, and environmental monitoring
• Apply state-of-the-art AI/ML techniques — including deep learning, transfer learning, self-supervised learning, and large vision/language models — to automate remote sensing data exploitation and analysis workflows
• Conduct rigorous uncertainty quantification, validation metric development, and statistical performance benchmarking across multi-source, multi-temporal geospatial datasets
• Collaborate with program managers, government customers, and interdisciplinary engineering teams to translate operational requirements into validated technical solutions
• Author technical reports, white papers, and briefings documenting methodology, V&V results, and performance findings for government sponsors

Remote Sensing Engineer