Artificial Intelligence Research Scientist

2 days ago

Perth, Western Australia beBeeResearch Full time $120,000 - $160,000
AI Researcher Opportunity

This role 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.


Requirements

We are seeking a highly skilled researcher who meets the following requirements:

  • 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 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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