Embedded Machine Learning Engineer

An embedded machine learning engineer is responsible for developing, deploying, and optimising machine learning models on resource-constrained embedded devices, processing sensor data in real time. They work on transforming raw, noisy sensor streams into robust, real-time classification models that operate within strict hardware limitations on memory, computation, and power. Their role involves collaborating with firmware, hardware, and data science teams, managing the full model lifecycle from data ingestion to field validation, and iterating models based on field data. The engineer must balance accuracy with hardware constraints and implement solutions that are reliable in real-world conditions. They contribute to cross-domain model transferability and optimise models through quantisation and pruning techniques. The role demands strong technical skills in Python, machine learning frameworks, and embedded systems, alongside excellent communication skills.