Machine Learning Embedded Software Engineer in Central

Embedded Machine Learning and Real-Time Sensor Classification Introduced by: KO2 Embedded Recruitment Solutions 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 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 engineering challenge is significant. You will be responsible for the complete lifecycle of machine learning models that run on embedded devices. You will work with raw sensor data streams from physical devices operating in uncontrolled environments. This data is not clean. Every machine learning decision you make has downstream consequences for embedded systems. 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. You will take models from lab development through semi-field validation and into live deployment. 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. The long-term vision is building a platform where machine learning models and data structures developed for one application can be adapted and transferred to others. This is the frontier of applied ML. Reporting to the Lead Data Scientist, you will own the development and deployment of machine learning models that sit at the heart of the embedded detection platform. You will work closely with R&D scientists, firmware engineers, hardware designers, and product teams. You will be the bridge between data science and embedded systems. You will own the complete pipeline: from sensor data ingestion and cleaning, through feature engineering and model development, through embedded optimisation and deployment, through field validation and iteration. Clean, structure, and analyse sensor datasets from real-world deployments for training and evaluation Develop machine learning models optimised for embedded deployment on resource-constrained devices Work with TensorFlow Lite, Edge Impulse, or custom firmware deployment strategies to integrate models into actual hardware platforms Collaborate with firmware engineers to integrate your models into live devices, understanding and accommodating their constraints Test model performance across lab, semi-field, and real-world settings; Document training pipelines, feature engineering methods, model validation results, and deployment learnings 5+ years of applied experience in data science or machine learning engineering roles ~ Strong, demonstrable experience with machine learning for classification tasks ~ Proficiency in Python and relevant libraries: scikit-learn, TensorFlow, pandas, NumPy ~ Real-world experience working with sensor data, time-series data, or IoT data streams ~ Familiarity with embedded ML tools and approaches: comfortable getting close to hardware, firmware code, and the real-world constraints of device deployment ~ Experience with dataset versioning and ML workflow management: Models in the Field, Not the Lab. You will have taken at least one machine learning model from initial development through to embedded deployment on a live device. You will have validated that model across lab conditions, semi-field conditions, and real-world deployment. A Repeatable ML Pipeline. The process will no longer be ad hoc. When new sensor data arrives, when a new classification task emerges, or when an existing model needs retraining, the team will have a clear, reproducible path forward. You will anticipate firmware engineer constraints before they surface as integration bottlenecks. You will understand that deployment is when learning truly begins. You will have learned the hard lessons about what separates a model that works in the lab from one that works reliably in the field. You will have contributed technical evidence that machine learning systems can be developed for one application and transferred to others, with appropriate adaptation and retraining. A cover letter (required) explaining your experience with embedded ML and real-world sensor data, and why this particular challenge interests you Can you commit to 3 days per week in Edinburgh? Describe a time you deployed a machine learning model to an embedded or edge device. Have you worked with real-world sensor or time-series data? Tell us about a time you took a machine learning model or pipeline built for one application and adapted it to a different use case or domain. What draws you to embedded machine learning specifically, rather than traditional cloud-based ML or data science