Embedded Machine Learning Engineer

An embedded machine learning engineer is responsible for developing and deploying models on resource-limited hardware, handling raw sensor data, and ensuring reliable real-time classification. They optimise models for embedded deployment considering memory, power, and latency constraints, working closely with hardware and firmware teams. The role involves iterating models in real-world conditions, diagnosing performance issues, and adapting solutions for different applications. Candidates should possess extensive experience with sensor data, embedded ML tools, and Python libraries. They are expected to communicate effectively with multidisciplinary teams and to understand the nuances of hardware limitations. The engineer plays a critical role in transforming sensor data into actionable insights through efficient, robust embedded machine learning models.