Computer Vision Engineer - VisionAI (AU)

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

• 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

Requirements

• 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 version control systems (e.g., Git) and workflow automation tools (e.g. GitLab CI, ClearML)
• Strong problem-solving skills, attention to data quality, and effective cross-team communication
• An abundance of ambition and motivation to grow the company and compete with the big players in the industry

Nice to have

• Experience programming control algorithms for electromechanical systems is preferred
• Experience modelling dynamic systems with mathematical representations using first-principles and system identification techniques is preferred
• Knowledge of object tracking algorithms (e.g., Kalman filters, optical flow) and/or control theory is preferred
• Previous ‘start-up’ or ‘scale-up’ experience is preferred