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Computer Vision Engineer (AU)
DroneShieldSydney, AustraliaPosted 29 April 2026
Job Description
Computer Vision Engineer (AU)
Work with cutting edge AI technology, making the world a safer and more secure place. DroneShield (ASX:DRO) offers an opportunity to solve some of world’s most challenging technical problems in the rapidly growing counter-drone sector.
Our customers operate in some of the most challenging and high-stakes environments in the world, including military organisations, government agencies, airports, critical infrastructure operators, and law enforcement. Protecting airspace in these settings requires technology that performs under pressure and teams that understand what’s at stake. At DroneShield, employees work at the leading edge of counter-drone innovation, helping to address real-world security challenges as drone threats continue to evolve globally.
With one of the largest listed defence company market capitalisations in Australia, now part of the ASX200 index, DroneShield is experiencing a period of hypergrowth. Revenue has surged from A$57 million in 2024 to over A$217 million in 2025, representing growth of more than 400% year-on-year, with record profitability and cashflow. The total addressable global market for counter-drone is assessed at approximately $100 billion, and is currently at the nascent stage with much of the growth still to come, with DroneShield well positioned as a global market leader, and the only public listed pure-play business in this sector.
The company has grown from 11 employees in 2017 to over 450 staff globally today, and is on track to reach around 550 by the end of 2026. This expansion includes investment of over A$50 million annually in R&D, a global pipeline exceeding A$2.5 billion, and continuous scaling of production capacity to meet accelerating demand.
The role is based at DroneShield’s central Sydney headquarters. Overseas on-the-ground presence includes Virginia (USA), Netherlands, Denmark, Mexico and Dubai, as well as distributors in over 70 countries worldwide.
About the role
In this role, you will be at the forefront of advancing our computer vision capabilities by integrating cameras and algorithms to build our next-generation AI-powered detection, identification, and tracking optical systems.
You’ll collaborate closely with system engineers, machine learning engineers, and product teams to ensure our product is performant, reliable, and optimised for real-world defence applications. This position offers the opportunity to work on unique, mission-critical challenges where your contributions directly impact global security outcomes.
Responsibilities, Duties and Expectations
Design, implement, benchmark, and optimise computer vision algorithms to enhance detection, classification, and tracking capabilities
Develop and adapt software interfaces to support the integration of new camera models and configurations
Collaborate with cross-functional teams to define technical requirements and ensure seamless integration of computer vision solutions
Qualifications, Experience and Skills
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
More than 3 years of experience in software engineering
Proficiency in C++ development, including multi-threading, unit testing, benchmarking, and profiling
Experience programming in Python is required, and familiarity with Golang is preferred
Experience with computer vision libraries (e.g. OpenCV) or integrating AI models (e.g. ONNX, TensorRT)
Experience integrating cameras or similar sensors (e.g., LiDAR, thermal cameras)
Experience with edge computing or embedded systems for real-time applications (e.g. NVIDIA GPUs, CUDA, GStreamer)
Experience with AI/ML engineering is preferred
Experience with version control systems (e.g., Git) and workflow automation tools (e.g. GitLab CI, ClearML)
Previous ‘start-up’ or ‘scale-up’ experience is preferred
Strong problem-solving skills, attention to data quality, and effective cross-team communication
An abundance of ambition and mot ... (truncated, view full listing at source)
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