Our client, a leading name in the education sector, is seeking an experienced AI / Machine Learning Researcher for an initial 3-month contract, with the potential for further 3-month extensions.
In this role, you will build upon and enhance existing video analytics capabilities, focusing on improving the accuracy, robustness, and efficiency of current models. This position uniquely combines applied AI research with real-world traffic engineering and safety analysis, offering the opportunity to work on impactful projects at the intersection of cutting-edge technology and transportation safety.
Key Responsibilities:
* Investigate, benchmark, and optimise model architectures for detection, segmentation, and tracking in complex transport environments.
* Enhance multi-object tracking pipelines using advanced methods such as DeepSORT, ByteTrack, and Transformer-based approaches.
* Improve video analytics performance through evaluation of alternative models and pipeline optimisation.
* Develop post-processing logic for trajectory denoising, smoothing, and error reduction.
* Apply denoising techniques to improve low-resolution or low-bitrate footage.
* Conduct field validation of model performance across diverse environments and camera types.
* Support camera calibration workflows for accurate spatial measurements.
* Run ablation studies and model evaluations under varied environmental conditions.
* Collaborate with cloud engineering teams for scalable inference deployment using ONNX, TensorRT, Triton, or similar.
* Translate AI outputs into transport safety and engineering metrics.
* Contribute to documentation, reports, and publications.
Essential Criteria:
* PhD in Computer Science, Applied Mathematics, Robotics, Physics, or related discipline; or equivalent experience with a strong applied AI/computer vision track record.
* Expertise in object detection, segmentation, and multi-object tracking in complex scenarios.
* Proficiency with deep learning frameworks (PyTorch, TensorFlow) and Python libraries (OpenCV, Yolo, NumPy, scikit-learn).
* Proven experience in real-world video analytics and model deployment under challenging conditions.
* Experience with model optimisation and inference acceleration.
* Familiarity with denoising techniques for low-quality video.
* Understanding of camera calibration and spatial measurement.
* Strong communication skills and ability to work independently and in multi-disciplinary teams.
Desirable:
* GPU-based inference deployment (ONNX, TensorRT, Triton).
* Knowledge of transport/traffic datasets.
* Track record of publications or open-source contributions.
* MLOps tools and practices (MLflow, Weights & Biases).
* Experience with Transformer architectures for video data.
To be considered for the role click the 'apply' button or for more information about this and other opportunities please contact Nicole Oo via email: noo@paxus.com.au
Paxus values diversity and welcomes applications from Indigenous Australians, people from diverse cultural and linguistic backgrounds and people living with a disability. If you require an adjustment to the recruitment process, including the application form in an alternate format, please contact me on the above contact details.
#J-18808-Ljbffr