Computer Vision Engineer Job at Dedrone, Sterling, VA

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  • Dedrone
  • Sterling, VA

Job Description

As a Machine Learning Engineer at Dedrone, you'll play a pivotal role in advancing airspace security. Leveraging cutting-edge camera systems and machine learning algorithms, you will help us detect, track, and classify diverse flying objects in complex airspace environments. By innovating detection and tracking techniques, you'll be key in developing our systems to respond to evolving airspace threats—from quadcopters and drones to other larger or unconventional aircraft. Working within a highly skilled team, you'll gain access to world-class resources and an extensive, unique dataset, providing a one-of-a-kind opportunity to shape the future of airspace security. What You'll Do: Develop, test, and optimize machine learning algorithms for detecting, tracking, and classifying various flying objects using camera systems. Innovate search and tracking methodologies in the context of a networked system of cameras, leveraging computer vision and potentially cognitive radar techniques. Collaborate with the simulation team to create scenarios for algorithm testing and evaluation Deploy algorithms onto edge compute devices using Python and C++. Keep pace with industry advancements by attending conferences and collaborating with leading experts. Contribute to a continuously evolving codebase, ensuring scalability and adaptability for future airspace challenges. What You Bring: Experience deploying computer vision algorithms like YOLO and RCNN 5 years of experience with Python programming language and object-oriented design methods expertise. Basic familiarity with tracking algorithms, such as Kalman filter, particle filter, and data association. Proven experience in computer vision and detection algorithms. Strong background in computer science, particularly in search algorithms. Proficiency in Python and C++ for both development and deployment of algorithms on edge devices. Familiarity with reinforcement learning and a passion for airspace security innovations. Excellent problem-solving skills and a track record of applying scientific methods to develop and improve algorithmic solutions.

Job Tags

Full time,

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