Embedded Machine Learning Engineer

Embedded Machine Learning and Real-Time Sensor Classification

Introduced by: KO2 Embedded Recruitment Solutions

Client Location: Edinburgh

Salary: £60,000 to £70,000 per annum

The Opportunity

Our client is building the next generation of real-time detection systems that operate at the edge of the network, where connectivity is unreliable and power is constrained. Their vision is straightforward and radical: machine learning models that run on embedded devices, processing sensor data in the field, making instant decisions without relying on cloud infrastructure or continuous data transmission.

This is not a supervised learning problem on tabular business data. This is not model serving from a GPU cluster. This is embedded machine learning in its most demanding form: taking sophisticated sensor-based classification systems and making them run reliably on devices with megabytes of RAM, in real-world environments where data is noisy, conditions are uncontrolled, and failure is not an option.

The client is at the frontier of what embedded ML can do. Most organisations are still building cloud-first systems. They are building ground-truth systems: devices that operate autonomously, make decisions in real time, and remain reliable under the constraints that define actual field deployment.

The Challenge

The engineering challenge is significant. You will be responsible for the complete lifecycle of machine learning models that run on embedded devices. This means:

From Sensor to Deployed Device. You will work with raw sensor data streams from physical devices operating in uncontrolled environments. This data is not clean. It carries noise, calibration drift, temperature sensitivity, and the unpredictable behaviour of systems deployed in the real world. Your job is to transform this stream into a robust, real-time classification model that runs on hardware with severe constraints on memory, computation, and power.

Engineering Under Constraint. Every machine learning decision you make has downstream consequences for embedded systems. A model that requires 512MB of RAM will not run on a device with 128MB. A model that takes 500ms per inference will drain the battery in days. A model that works in the lab but degrades in the field is a fa...