Embedded Machine Learning Engineer
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The role
An embedded machine learning engineer develops and deploys models on resource-constrained hardware for real-time sensor data classification. The role involves handling noisy, real-world data and transforming it into robust models within strict constraints on memory, computation, and power. The engineer iterates models from lab to field, debugging and improving performance based on deployment feedback. Collaboration with cross-disciplinary teams ensures seamless integration of models into hardware, considering firmware and system limitations. Technical skills include sensor data processing, model optimisation, and deployment using tools like TensorFlow Lite and Edge Impulse. This position demands a focus on real-world reliability, efficiency, and scalable workflows for applicable fields.
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