About the Role

We're building AI-powered solutions that run directly on physical hardware and edge devices wearable devices and vehicle-based systems among them. This is a hands-on engineering role for someone who's comfortable at the intersection of hardware and software, getting AI inference running reliably under real-world constraints of power, memory, latency, and environmental conditions.

You'll lead the technical development of our first wearable AI device deployment, then extend that capability to sensor-based systems in vehicle and industrial contexts as the program grows.

What You'll Do

•     Lead end-to-end technical development of an AI enabled wearable device solution, from hardware integration through to field deployment

•     Manage hardware-software integration, including device SDKs, drivers, firmware interfaces, and sensor APIs

•     Design and implement AI inference pipelines optimised for edge compute constraints power, memory, and latency

•     Evaluate, select, and implement edge AI frameworks such as ONNX, TFLite, TensorRT, or similar

•     Develop and execute hardware-in-the-loop testing and validation protocols

•     Extend the platform's edge AI capability to vehicle and sensor-based systems, including integration with onboard diagnostics and telematics data

•     Document hardware integration processes, deployment playbooks, and maintenance procedures

•     Stay current with developments in edge AI hardware, wearable computing, and embedded AI frameworks

What We're Looking For

Required:

•     Degree in Computer Engineering, Electrical Engineering, Computer Science, or a related discipline

•     1–3 years' experience in embedded systems, edge AI or hardware-software integration

•     Hands-on experience with embedded or edge devices wearables, IoT hardware, industrial devices, or similar

•     Proficient with hardware communication protocols: I2C, SPI, UART, BLE, and device SDK integration

•     Demonstrated experience deploying ML/AI models on resource-constrained or edge hardware

•     Able to debug at the hardware-software boundary using appropriate embedded debugging tools

•     Proficient in C/C++ for embedded systems, and Python for AI/ML pipeline development

Preferred:

•     Experience with vehicle sensor systems or automotive telematics CAN bus, OBD-II, or similar in vehicle data protocols

•     Experience with computer vision pipelines object detection, image classification, or scene understanding

•     Familiarity with AR or wearable hardware platforms

•     Exposure to Io platforms and sensor network architectures

•     Experience with modern AI-assisted development tooling

•     Prior work in field deployment of edge AI solutions in an industrial or logistics environment

What We Offer

•     The opportunity to own and shape a strategic, growing capability from the ground up

•     Direct collaboration with senior technical leadership on solution architecture and deployment strategy

•     A hands-on role spanning both cutting-edge software and real physical hardware deployment