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Job Description
- Design, train, and evaluate novel AI-based architectures for on-device sensor fusion, gesture recognition, and continuous physiological biosignal monitoring (e.g., IMU, optical sensors).
- Own the full model optimization lifecycle apply advanced knowledge distillation, quantization, and pruning techniques to adapt deep learning models into ultra-low-power formats (TFLite Micro) for efficient, micro-watt edge inference.
- Develop and maintain high-performance, real-time constrained architecture sensor pipelines in C/C++, optimizing heavily for latency, power consumption, and memory footprint on wearable MCUs.
- Collaborate with hardware, firmware, product, and UX teams to influence the design of next-generation health sensors and hardware abstraction layers, ensure guaranteed-by-design experience coupled with the physical components.
- Bridge the gap between research scientists and embedded systems engineering, serving as technical lead and subject matter expert for on-device sensor algorithms to guide architectural decisions and define the long-term technical roadmap.