Remote Sensing Software Engineer

Booz Allen Hamilton•Dayton, OH

1d•$69,300 - $158,000•Remote

About The Position

At Booz Allen, you’ll join a team using advanced imaging, physics-based modeling, and AI/ML to solve some of the nation’s most compelling Space Domain Awareness (SDA) and remote sensing challenges. You’ll contribute to the design and development of high-fidelity processing pipelines that transform raw sensor data into insight. You’ll join a collaborative technical delivery team where you’ll contribute to secure, reliable, and user-centric tools that accelerate mission outcomes.

Requirements

4+ years of experience with remote sensing, image processing, or GEOINT data exploitation
Experience designing, modifying, developing, writing, and implementing software programming applications using Agile methods
Experience with Python and scientific Python packages such as NumPy, SciPy, OpenCV, or xarray
Experience with basic orbital mechanics
Experience with ML frameworks such as PyTorch or TensorFlow
Experience with large sensor datasets such as HDF5 or cloud object storage
Active TS/SCI clearance; willingness to take a polygraph exam
Bachelor’s degree in a STEM field

Nice To Haves

Experience with administration of continuous integration or continuous deployment (CI/CD) pipelines using Kubernetes, Docker, or Jenkins
Experience with sensor phenomenology, radiometric calibration, or remote sensing interpretation
Experience with super-resolution, deblurring, spectral exploitation, or resolved feature extraction
Experience with uncertainty quantification, Kalman filtering, or tracking algorithms
Experience with AI-augmented development workflows or agentic tools
Experience with GitLab-based CI/CD workflows
Master’s degree in Physics, Mathematics, CS, or a related STEM field

Responsibilities

Develop software tools for radiometric modeling and satellite observation geometry.
Implement imaging and signal processing workflows, including motion compensation, multi-frame alignment, and super-resolution.
Build ML-enabled detection, anomaly identification, and object classification modules using operational EO/IR and synthetic datasets.
Design scientific Python pipelines such as NumPy, OpenCV, Astropy, or SciPy, for real time and batch processing.
Support orbital modeling tasks such as SGP4, TLE ingestion, or basic pose estimation.
Conduct imagery preprocessing such as radiometric calibration, artifact correction, and multi-frame filtering.
Maintain reproducible environments using GitLab, containers, and experiment-tracking tools.
Collaborate with astrodynamics SMEs, operations personnel, and ML engineers to deliver integrated mission capabilities.