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

Requirements

Active U.S. Citizenship
Must be able to obtain and maintain a Top Secret security clearance with SCI access; ability to obtain program-specific clearances as required
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
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.

Benefits

Comprehensive compensation and benefit packages

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdeep learningtransfer learningself-supervised learninglarge vision modelschange detectionland cover classificationobject detectionstatistical modelinguncertainty quantification

Soft Skills

communication skillscollaborationtechnical writing

Certifications

Top Secret security clearanceSCI access