At a Glance
- Tasks: Create cutting-edge machine learning models for real-time sensor classification on embedded devices.
- Company: Join a pioneering tech firm at the forefront of embedded machine learning.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic role with significant challenges and opportunities for innovation.
- Why this job: Make a real impact by developing autonomous systems that operate in challenging environments.
- Qualifications: Experience in machine learning and embedded systems is essential.
The predicted salary is between 60000 - 70000 £ per year.
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 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.
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 failure. You will learn to think like an embedded systems engineer, not a researcher.
- Real-World Iteration: You will take models from lab development through semi-field validation and into live deployment. You will see what happens when your model encounters conditions it has never seen before. You will debug why a model that performed flawlessly during development is behaving unexpectedly in the field. You will iterate based on real-world performance data and build the judgment to know when a model is sufficiently reliable for deployment.
Embedded Machine Learning Engineer in London employer: KO2 Embedded Recruitment Solutions LTD
Our client in Edinburgh is an exceptional employer, offering a unique opportunity to work at the cutting edge of embedded machine learning technology. With a strong focus on innovation and real-world applications, employees benefit from a collaborative work culture that encourages growth and continuous learning. The company provides competitive salaries, a supportive environment for professional development, and the chance to make a significant impact in the field of autonomous systems.
Contact Details:
KO2 Embedded Recruitment Solutions LTD Recruitment Team
