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Sr. Computer Vision Engineer - Visual Target TrackingTeledyne FLIR • Orem, Utah, United States
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Sr. Computer Vision Engineer - Visual Target Tracking

Teledyne FLIR
  • US
    Orem, Utah, United States
  • US
    Orem, Utah, United States
Apply Now

About

We are a fast-growing, mission-driven company developing advanced military Unmanned Aerial Vehicle (UAV) systems for the U.S. Department of Defense. Our platforms operate in challenging real-world environments and support capabilities such as autonomous navigation, precision guidance, visual target tracking, GPS-denied operation, path planning, and obstacle avoidance. We are seeking a Sr. Computer Vision Engineer to develop and improve our multi-camera visual target tracking algorithms for UAV systems. The primary focus of this role is building robust computer vision algorithms that track moving targets across multiple cameras and challenging environments. This role involves integrating modern deep learning object detectors with classical computer vision, evaluating performance using flight data, and deploying production-quality algorithms on embedded systems. You will work across the full development pipeline—from algorithm design and simulation to flight testing on operational platforms.
Job Duties & Responsibilities
Develop and improve visual target tracking algorithms for UAV systems using camera and sensor data.
Integrate modern deep learning object detectors, classifiers, and re-identification models (e.g., YOLO-style detectors) to improve tracking robustness.
Maintain target identity as objects move across multiple camera fields of view without relying on geolocation.
Evaluate and refine algorithms using flight data, simulation, and controlled testing to ensure robustness to edge cases.
Implement high-performance algorithms in C++ for deployment on embedded Linux platforms.
Work closely with flight test engineers and system developers to validate performance through simulation, analysis, and live flight tests.
Support development of robotics algorithms for state estimation, navigation, and guidance.
Fuse data from sensors including cameras, IMUs, GPS, magnetometers, laser range finders, and barometers.
Contribute to integration with PX4-based flight control systems.
Support integration and testing of new sensors and payloads.
Contribute to embedded software components such as sensor interfaces, timing, and synchronization.
Job Qualifications
Master’s degree or higher in Computer Science, Electrical Engineering, Robotics, Mechanical Engineering, or a related field strongly preferred.
Strong background in computer vision and camera models.
4+ years of experience developing computer vision or perception algorithms.
4+ years of experience working on robotics, autonomous systems, or aerospace platforms.
Experience implementing robotics algorithms such as visual tracking, state estimation (e.g., Kalman filtering), perception pipelines, or vision-based deep learning integration.
Strong software development skills in C++, Python, and C.
Experience developing software for Linux-based embedded systems.
Experience working with sensors such as IMUs, cameras, magnetometers, GPS, laser range finders, and barometers.
Understanding of feedback control systems and practical tuning methods.
Experience integrating deep learning object detectors (e.g., YOLO-style detectors) into perception pipelines preferred.
Experience with visual SLAM or visual odometry systems preferred.
Experience working with multi-camera systems or multi-object tracking preferred.
Experience with UAV flight testing or aerial robotics platforms preferred.
Applicants must be a U.S. citizen, U.S. national, lawful permanent resident, asylee, refugee, or otherwise eligible to obtain the required U.S. export control authorization from the Departments of State or Commerce.
Applicants must be a U.S. citizen, U.S. national, lawful permanent resident, asylee, refugee, or otherwise eligible to obtain the required U.S. export control authorization from the Departments of State or Commerce.
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  • Orem, Utah, United States

Languages

  • English
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