Enhance Video Analytics Capabilities

5 days ago

Perth, Western Australia beBeeMachineLearning Full time $100,000 - $150,000
Video Analytics Researcher

We are seeking an experienced Video Analytics Researcher to enhance our existing video analytics capabilities. In this role, you will improve model accuracy, robustness, and efficiency.

About the Role:

  • You will investigate, benchmark, and optimize model architectures for detection, segmentation, and tracking in complex environments.
  • You will work on enhancing multi-object tracking pipelines using advanced methods such as DeepSORT, ByteTrack, and Transformer-based approaches.
  • You will improve video analytics performance through evaluation of alternative models and pipeline optimization.
  • You will develop post-processing logic for trajectory denoising, smoothing, and error reduction.
  • You will apply denoising techniques to improve low-resolution or low-bitrate footage.
  • You will conduct field validation of model performance across diverse environments and camera types.
  • You will support camera calibration workflows for accurate spatial measurements.
  • You will run ablation studies and model evaluations under varied environmental conditions.
  • You will collaborate with cloud engineering teams for scalable inference deployment using ONNX, TensorRT, Triton, or similar.
  • You will translate AI outputs into transport safety and engineering metrics.

Requirements:

  • A 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 optimization 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.

Preferred Qualifications:

  • 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.

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