ML Engineer – Computer Vision: Build smarter, safer care technology

About this job

We’re looking for a Machine Learning Engineer This role sits at the core of Sensio’s sensor-driven care technology, and is placed within our new AI team. You will work with machine learning models that run on embedded devices, close to the hardware, in products that need to perform reliably in real care environments. Sensio’s technology uses advanced sensors, algorithms and embedded systems to support digital supervision and detect potentially dangerous situations. Sensio’s technology generates a growing range of real-time signals, such as falls, noise and activity in a resident’s room. Other products and integrations contribute alarm data, events data and data from care environments both inside and outside of Sensio’s own ecosystem. As a Machine Learning Engineer, you will work mainly on training and evaluating Computer vision models. The models will make the best possible use of the hardware available, balancing performance, precision, robustness, memory, power consumption and deployment constraints. We are looking for someone who understands the full journey from data and training to validation, deployment, hardware performance and continuous improvement. You will join a strong product and engineering environment where hardware, software, data and machine learning work closely together. Your work can directly affect the safety of residents, the working day of healthcare professionals and the future of care technology. Your day-to-day You will be an instrumental part of the team developing, improving and deploying machine learning models for Sensio’s sensor-driven care technology. Your daily work will include: Training, validating and improving computer vision models Optimizing models for embedded devices and resource-constrained hardware, including performance, memory use and power consumption Working with sensor data, including depth, time-of-flight or similar data sources, to improve model precision and robustness Exporting, testing and preparing models for deployment Building systems for continuous improvement, experiment tracking, model evaluation and feedback loops Collaborating closely with embedded, software, product and data science colleagues to ensure that models work reliably in real-world care environments This is what we’re looking for We are looking for a machine learning engineer with strong experience in computer vision and model development for embedded or resource-constrained environments. This is a senior role that requires strong technical depth and the ability to work independently with complex machine learning problems, whether that expertise comes from industry, research or a combination of both. If you’re the right candidate for the job, you probably have: A master’s degree or PhD in machine learning, computer science, robotics, signal processing or a related field Solid experience with computer vision Experience developing, training, validating and optimizing ML models for embedded devices or resource-constrained hardware Strong Python skills and experience with ML frameworks such as PyTorch Experience working close to hardware, preferrably with sensors, depth cameras, time-of-flight data, robotics, drones, automotive systems or similar Understanding of inference on edge hardware This job does not require that your background is within care or healthcare technology, but you do need to understand what it means to build models that have to work reliably outside of a controlled lab environment. Bonus points if you have experience with one or more of the following: Time-of-flight cameras, depth sensors, spectrum data or sensor fusion Embedded Linux, C++ or working closely with firmware or embedded software teams Model profiling, memory optimization or low-power ML deployment Hardware-in-the-loop testing, automated validation or continuous model evaluation MLOps, experiment tracking, dataset versioning or model lifecycle tools such as MLflow Safety-critical, privacy-sensitive or regulated products where reliability mat

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