Senior Computer Vision Algorithm/Software Engineer

SAAZ is seeking an exceptional Senior Computer Vision Algorithm & Software Engineer to architect and implement advanced processing pipelines for next-generation electro-optical and infrared (EO/IR) imaging systems.

This multidisciplinary role bridges theoretical research and high-performance software engineering, requiring hands-on expertise in image processing, computer vision, convolutional neural networks (CNNs), or spiking neural networks (SNNs) rather than traditional application software development alone. Operating with a high degree of autonomy, the successful candidate will hold a graduate degree—preferably a PhD—and possess the independent drive to conceptualize, design, and deploy sophisticated algorithms from scratch without direct supervision.

Working closely with Firmware, FPGA, Systems, and Product Engineering teams, you will drive algorithmic innovation for advanced camera products deployed in aerospace, defense, and commercial imaging, guiding developments from concept through hardware-software integration and production.

Key Responsibilities:

• Algorithm Architecture & Conceptualization: Design, prototype, and refine advanced image processing and computer vision algorithms—including traditional image enhancement, noise reduction, and modern deep learning models (CNNs/SNNs)—tailored for EO/IR sensor architectures.

• Autonomous End-to-End Implementation: Independently translate mathematical models and theoretical concepts into high-performance, maintainable software implementations without needing direct step-by-step supervision.

• Cross-Functional System Integration: Collaborate closely with Firmware, FPGA, and Systems Engineering teams to optimize, port, and validatealgorithms on real-time target hardware and embedded camera processing platforms.

• EO/IR Pipeline Optimization: Develop and tune edge-detection, feature extraction, non-uniformity correction (NUC), dynamic range expansion, object detection

and tracking algorithms specialized for complex electro-optical and infrared environments.

• Research & Feasibility Trade Studies: Conduct independent trade studies, literature reviews, and rapid prototyping to evaluate novel machine learning and spiking neural network (SNN) approaches for low-power or bandwidth-constrained imaging systems.

• Verification & Testing Pipelines: Build robust simulation environments, ground-truth dataset collection methodologies, and automated testing frameworks to evaluate algorithm accuracy, latency, and performance edge cases.

• Technical Documentation & Mentorship: Document mathematical formulations, algorithmic trade-offs, and software architectures to support production handover, system qualification, and intellectual property development.