Senior Imagery Scientist – EO (TS/SCI)

Xcellent Technology Solutions•Springfield, VA

About The Position

Artificial intelligence is rapidly changing how GEOINT is collected, analyzed, and operationalized. But before AI can support mission-critical decisions, it must be tested, validated, and trusted under real operational conditions. This is a special opportunity to join us as an Electro-Optical (EO) Imagery Scientist to support the National Geospatial-Intelligence Agency’s (NGA) Assurance and Governance Office (QA) Accreditation of AI Models (AGAIM) program. Looked upon your expertise to help evaluate and validate AI-enabled GEOINT capabilities against operational imagery datasets to ensure AI systems perform reliably in the environments where mission success matters most. Working at the intersection of GEOINT tradecraft, remote sensing science, and AI/ML evaluation. You’ll analyze how environmental effects collection geometry, sensor limitations, and image quality impact algorithm performance to designing analytic methodologies and validation frameworks that test AI systems beyond ideal conditions. Your expertise in EO phenomenology, collection systems, and image exploitation will directly shape how NGA accredits and operationalizes emerging AI capabilities across the enterprise. If you are ready to apply your expertise and directly influence the future of operational AI within national security, apply today!

Requirements

Current active Top Secret / SCI clearance with the willingness to obtain CI poly.
PhD + 10 years / Master's degree + 13 / Bachelor’s degree + 15 / Associate's degree + 16 / 18+ years of relevant experience.
Deep expertise in EO collection systems, phenomenology, and image formation to properly evaluate AI performance as understanding how imagery is created, how sensors behave, and how environmental conditions affect what an algorithm ‘sees’.
Strong experience evaluating AI/ML algorithms using operational EO datasets to support in assessing whether an algorithm performs reliably across the variability complexity of real-world GEOINT collection.
Experience exploiting EO imagery to determine occurrence and location of objects of interest (OOIs), in order to evaluate whether AI outputs are analytically meaningful.
Experience developing, testing, or evaluating analytic methodologies or algorithms to support methodology design, testing framework, and measurable performance standards.
Proficiency with Python, MATLAB, and/or Google Earth Engine.
Deep understanding of remote sensing principles and GEOINT tradecraft.
Experience applying image quality metrics and assessing environmental or sensor impacts in order to understand how haze, shadows, off-nadir angle, weather, compression, or sensor limitations affect algorithm confidence and reliability.

Nice To Haves

Experience with computer vision and machine learning applied to EO imagery

Responsibilities

Evaluate and validate AI-enabled GEOINT capabilities against operational imagery datasets to ensure AI systems perform reliably in the environments where mission success matters most.
Analyze how environmental effects collection geometry, sensor limitations, and image quality impact algorithm performance.
Design analytic methodologies and validation frameworks that test AI systems beyond ideal conditions.
Exploit EO imagery to determine occurrence and location of objects of interest (OOIs), in order to evaluate whether AI outputs are analytically meaningful.
Develop, test, or evaluate analytic methodologies or algorithms to support methodology design, testing framework, and measurable performance standards.
Handle large operational datasets, test analytic workflows, and assess model outputs.