About the Job We’re looking for Machine Learning Engineer for computer vision to join our high-impact applied research team. This role will play a key part in pushing the boundaries of our real-world video analytics platform, focused on improving traffic safety and transport insights through state-of-the-art computer vision and deep learning techniques. You’ll lead hands-on development of detection, segmentation, and multi-object tracking models designing and testing innovations that will directly improve outcomes for traffic engineering and transport policy. This is a deeply technical role at the intersection of AI research and real-world application. You’ll be working alongside a close-knit team of engineers and researchers, with significant autonomy, access to real-world datasets, and opportunities to publish and share your work. The position is ideal for someone with strong research experience and a passion for applied impact. You Should Have A PhD in Computer Science, Applied Mathematics, Robotics, or a related discipline (Alternatively, a Master's/Bachelor's with a strong applied AI research track record) Proven expertise in object detection, segmentation, and multi-object tracking Experience with complex video analytics scenarios (e.g. occlusion, re-identification) Proficiency with PyTorch or TensorFlow, and core libraries like OpenCV and NumPy Demonstrated experience deploying and optimising deep learning models in real-world settings Experience with video artefact denoising and performance improvement techniques Strong understanding of camera calibration and spatial measurement workflows Ability to independently design and run ablation studies and field validations Excellent communication skills and ability to collaborate across technical domains Nice to Have Experience with ONNX, TensorRT, or Triton for GPU-based inference Familiarity with transport or traffic datasets and traffic engineering metrics Exposure to MLOps tools like MLflow or Weights & Biases Research background in Transformer-based architectures for video analytics Publication record or open-source contributions in computer vision or applied AI Key Responsibilities Investigate and benchmark model architectures for detection, segmentation, and tracking Enhance and optimise multi-object tracking pipelines using DeepSORT, ByteTrack, or Transformer-based approaches Evaluate and implement new model strategies to improve real-world video performance Optimise inference runtime and end-to-end latency across the video processing pipeline Apply advanced denoising techniques to mitigate artefacts from low-resolution or compressed footage Lead field validation of models using drone, fixed, and varied camera types Contribute to post-processing, trajectory smoothing, and spatiotemporal error reduction Support spatial calibration workflows for accurate measurement of real-world traffic data Collaborate with cloud and software engineering teams for scalable model deployment Translate AI outputs into actionable transport safety insights Document experimental findings and contribute to internal and external publications The Benefits Join a mission-driven team using AI to make transport safer and smarter Work on impactful projects with real-world deployment and policy relevance Hybrid or fully remote work options Access to cutting-edge tools, models, and real-world datasets Collaborative, multidisciplinary environment that values innovation and rigour Opportunity to publish, present, and contribute to open-source research How to Apply If this opportunity sounds like a strong fit, we’d love to hear from you. Please apply, and feel free to reach out via email at kent@versegroup.com.au or call Kent Sin on (08) 6146 4464. J-18808-Ljbffr
