Wearable embedded intelligent algorithm engineer

2. Implement algorithm optimization and transplantation on SoC chips;

3. Support hardware debugging and low-power optimization

Implement model compression, quantization, acceleration and deployment based on hardware platforms (Nordic / STM32 / ESP32 / Apollo / TI, etc.).
Collaborate with the algorithm team to convert MATLAB/Python prototype models to C/C++, RTOS or TinyML executable versions.
Optimize algorithm delay, memory usage and power consumption to ensure real-time requirements for low-power wearable devices.
Support sensor (PPG/ECG/pressure/temperature, etc.) data link for data acquisition, synchronization, filtering and real-time processing.
Assist the hardware team in product debugging, validation and mass production import.

Requirements:

1. Bachelor's degree or above in electronic engineering, computer engineering related fields;
2. Familiar with C/C++ programming, with ARM Cortex architecture development experience;
3. More than 3 years of relevant work experience or a master's degree is preferred.
Offline office does not support remote work.

Specific requirements: Proficient in embedded software development (C/C++), familiar with MCU architecture, low-power processing, and RTOS.
Have TinyML / TFLM / CMSIS-NN / Edge Impulse / ONNX Runtime Mobile, etc. Edge AI deployment experience is preferred.
Familiar with sensor data processing (PPG/ECG/IMU/pressure) is preferred.
Applicants with experience in wearable devices, medical electronics or IoT products are preferred.
Have algorithm understanding ability, be able to read and migrate ML/DL models to embedded platforms.

Have health monitoring devices, blood pressure algorithms, PPG/ECG signal processing experience.
Familiar with BLE, dynamic synchronization, power management, OTA, etc., the core modules of wearable devices.
Participated in medical-grade product development or clinical validation processes.