Embedded Machine Learning Engineer

An embedded machine learning engineer is responsible for developing models that operate in real-time on resource-constrained devices, handling noisy and uncalibrated sensor data in uncontrolled environments. They work on transforming raw sensor streams into reliable classification models that meet hardware constraints related to memory, computation, and power. Their role involves deploying models from lab to field, iterating based on real-world data, and ensuring robustness in diverse conditions. They collaborate with hardware and firmware teams to optimise models through quantization and inference speed enhancements. The engineer also contributes to building scalable workflows for model training, validation, and adaptation across different applications. Success is measured by deploying reliable models in real-world settings and establishing repeatable, efficient development pipelines.