Lead/Senior Lead Research Engineer

Responsibilities

• Design, develop, and implement AI models and pipelines for real-world applications in training, assessment, safety, operations, and decision-support.
• Develop AI solutions involving video analytics, object detection, image classification, visual attention analysis, speech-to-text, audio processing, communication analysis, and related applied AI tasks.
• Work with data from video, audio, sensors, simulators, operational systems, and other sources to develop AI-enabled analytics and assessment capabilities.
• Integrate AI models with software platforms, dashboards, simulators, sensor systems, training platforms, or decision-support tools to support practical deployment and operational use.
• Explore, evaluate, and prototype suitable AI techniques, including deep learning, sequence models, multimodal learning, natural language processing, and large language model applications where relevant.
• Lead AI components within R&D projects, manage technical milestones, prepare documentation, and ensure timely delivery of project outputs.
• Supervise and mentor AI engineers or junior team members, review technical approaches, guide model development, and support capability building within the AI team.
• Work with internal teams, academic staff, industry partners, and external stakeholders to co-develop AI solutions and apply AI capabilities across centre-, department-, and school-level initiatives.
• Present AI approaches, findings, prototypes, and project outcomes to management, project partners, and industry collaborators in a clear and practical manner.

Qualifications

• Tertiary Qualification in Computer Science, Artificial Intelligence, Computer Engineering, Data Science, or a related field.
• Strong hands-on experience in computer vision and/or speech recognition, including model development, training, evaluation, and deployment. Relevant technical experience may include video analytics, object detection, image classification, pose or behaviour recognition, visual attention analysis, speech-to-text, audio processing, speaker or communication analysis, natural language processing, or multimodal AI involving video, audio, sensor, simulator, or time-series data.
• Proficiency in Python and experience with AI/ML frameworks and toolkits such as PyTorch, TensorFlow, OpenCV, Hugging Face, or equivalent.
• Strong understanding of machine learning and deep learning model development, including data preparation, model training, evaluation, optimisation, and performance validation.
• Experience integrating AI models into software systems, dashboards, simulators, training platforms, or operational applications is preferred.
• Experience with Large Language Models (LLMs) is an added advantage, particularly in applications beyond standard chatbot or document Q&A systems, such as multimodal AI, speech/video understanding, automated assessment, structured information extraction, model evaluation, fine-tuning, or integration with operational software systems.
• Experience leading AI workstreams, supervising technical staff, or mentoring junior engineers is preferred.
• Strong problem-solving ability, project execution skills, communication skills, and ability to work with both technical and non-technical stakeholders.