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This role combines hands-on machine learning engineering with technical leadership, working across the full lifecycle of AI development - from model design and experimentation through to deployment on embedded platforms and integration into operational systems. You will work at the intersection of machine learning, computer vision, and real-world system deployment, contributing to technologies used in demanding operational environments.
Job Responsibility:
Design, train, and evaluate computer vision models including object detection, classification, and segmentation
Conduct structured experimentation and performance analysis to guide model architecture decisions
Optimise models for edge deployment across embedded hardware platforms
Convert and deploy models using frameworks such as TensorRT, ONNX, or TFLite
Support development of data pipelines, dataset curation, and annotation workflows
Benchmark and tune model performance for latency, accuracy, and operational constraints
Contribute to ML assurance practices including validation, benchmarking, and regression testing
Mentor engineers and contribute to the growth of the ML capability
Requirements:
Degree in Computer Science, Engineering, Data Science, or equivalent experience
Strong experience developing production machine learning systems
Hands-on expertise with deep learning frameworks such as PyTorch
Experience developing computer vision models
Nice to have:
Experience in senior or lead machine learning engineering roles
Experience deploying ML models to embedded or edge platforms (e.g. Jetson, Qualcomm)
Familiarity with MLOps tooling, CI/CD pipelines, and model lifecycle management
Experience working on real-time systems, robotics, aerospace, or defence-related technologies