Remote Sensing Software Engineer - Space Imaging & ML

Worky; Dayton (OH); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

401(k) matching

Employee Stock Ownership Plan (ESOP)

Medical, vision, and dental insurance

Paid leave and disability insurance

Bonuses

Job summary

CFD Research, based in Dayton, OH, seeks a Remote Sensing Software Engineer to build advanced SDA and remote sensing pipelines, guide technical direction, and collaborate with multi-disciplinary teams on high-fidelity space observation analytics.

You will architect end-to-end processing systems, implement ML-based anomaly detection, and integrate multi-sensor data using Bayesian or ML methods, leveraging Python, C++, CUDA, and HPC/cloud platforms.

Qualifications

• 3–8+ years of experience in remote sensing, image/signal processing, or GEOINT data exploitation.
• Bachelor’s degree in a related STEM field; Master’s or PhD preferred.
• Proficiency in Python and C++ with experience in Linux and AGILE development practices.
• Experience with ML systems for imagery processing, target detection, tracking, or object classification.

Responsibilities

• Architect end-to-end remote sensing processing pipelines and SDA workflows.
• Develop and integrate ML-based anomaly detection and classification modules.
• Design multi-sensor fusion pipelines (EO/IR, GEOINT, RF) using Bayesian or ML methods.
• Collaborate with multi-disciplinary teams to deliver scalable software on HPC or cloud platforms.

Skills

Remote sensing

Python

C++

AGILE development

Linux

ML for imagery

Education

Bachelor’s degree in STEM

Master’s or Ph.D. preferred

Tools

CUDA

HPC

Cloud computing

Git

Job description

CFD Research, based in Dayton, OH, seeks a Remote Sensing Software Engineer to build advanced SDA and remote sensing pipelines, guide technical direction, and collaborate with multi-disciplinary teams on high-fidelity space observation analytics.

You will architect end-to-end processing systems, implement ML-based anomaly detection, and integrate multi-sensor data using Bayesian or ML methods, leveraging Python, C++, CUDA, and HPC/cloud platforms.