Remote Sensing Engineer

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. They are seeking a Remote Sensing Engineer to support cutting-edge geospatial intelligence and AI/ML-driven data exploitation efforts for the U.S. Department of Defense. The successful candidate will be responsible for the automated verification and validation of remote sensing data pipelines and AI/ML models.

Responsibilities:

• 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
• Architect and execute data quality assessment (DQA) protocols for ingested satellite, airborne, and in-situ sensor data; document and communicate findings to program teams and stakeholders
• Collaborate with program managers, government customers, and interdisciplinary engineering teams to translate operational requirements into validated technical solutions
• Evaluate and integrate emerging remote sensing technologies and open-source AI/ML frameworks; assess vendor claims, algorithm documentation, and technical data packages
• Contribute to IRAD initiatives advancing CISG's remote sensing and autonomous sensing capabilities, including development of novel approaches for environmental monitoring, target detection, and geospatial change analytics
• Author technical reports, white papers, and briefings documenting methodology, V&V results, and performance findings for government sponsors
• Provide technical mentorship to junior engineers and researchers on remote sensing methods, ML best practices, and geospatial data science
• Stay current with advances in foundation models, multi-modal geospatial AI, and emerging remote sensing sensor modalities relevant to national security applications

Requirements:

• Active U.S. Citizenship (required for all Riverside Research positions)
• Must be able to obtain and maintain a Top Secret security clearance with SCI access; ability to obtain program-specific clearances as required. Candidates with an active TS/SCI are strongly preferred
• Bachelor's degree in Remote Sensing, Geospatial Science, Earth Systems, Electrical Engineering, Computer Science, or a closely related STEM field
• A minimum of 8 years of related experience with a Bachelor's degree, 6 years with a Master's degree, 3 years with a PhD, or equivalent combination of education and experience. Graduate research experience counts toward this threshold
• Demonstrated expertise in multispectral and/or hyperspectral remote sensing data analysis, including atmospheric correction, spectral indices, spectral unmixing, and feature extraction
• Proficiency in Python for geospatial data engineering, including experience with rasterio, rioxarray, xarray, GDAL, geopandas, NumPy, and scikit-learn
• Hands-on experience with machine learning and statistical modeling applied to remote sensing or geospatial datasets (e.g., classification, regression, anomaly detection, change detection)
• Experience developing and executing V&V or T&E processes for data products, software systems, or AI/ML models, including design of test plans, performance metrics, and acceptance criteria
• Familiarity with geospatial platforms and tools: ArcGIS Pro, QGIS, ENVI, and/or Google Earth Engine
• Experience with cloud-based geospatial workflows (AWS, Google Cloud, or Azure) and version control practices (Git/GitLab/GitHub)
• Strong written and verbal communication skills with demonstrated ability to present complex technical findings to both technical and non-technical audiences
• Active Top Secret/SCI clearance — candidates who already hold an active TS/SCI will be given strong preference and can expect an accelerated onboarding timeline
• Experience supporting DoD, Intelligence Community, or national security remote sensing programs (NRO, NGA, AFRL, ARO, or equivalent)
• Familiarity with hyperspectral sensing platforms (e.g., AVIRIS, PRISMA, orbital hyperspectral systems) and hyperspectral analytics pipelines including mineral characterization and vegetation health assessment
• Experience applying deep learning frameworks (PyTorch, TensorFlow, Hugging Face) to geospatial or computer vision tasks, including fine-tuning foundation models or geospatial FMs (e.g., Prithvi, SatMAE, Clay)
• Background in SAR processing, LiDAR analysis, or multi-modal sensor fusion for environmental or intelligence applications
• Experience with automated testing frameworks, CI/CD pipelines, and MLOps practices for geospatial AI/ML systems
• Track record of peer-reviewed publication, conference presentations (e.g., IGARSS, AGU, SPIE), or technical reports in remote sensing or geospatial AI
• Demonstrated experience in a lead or senior individual contributor role, including mentorship of junior technical staff and coordination across multi-disciplinary teams
• Familiarity with GEOINT tradecraft, NSDI standards, or DoD geospatial data standards (NTM, NITF, STANAG imagery formats)