Sensor Intelligence Engineer II (Embedded)
Boston, MAFull-TimeMid-levelSoftware Engineering
RESPONSIBILITIES:
- Optimize WHOOP wearables to maximize sensor performance vs power consumption. Develop and refine algorithms to control low-level hardware, interfacing with the embedded and hardware teams.
- Design and implement cutting-edge signal processing and machine learning algorithms on low-power MCUs and/or DSP processors, with a specific focus on ARM Cortex-M processors.
- Engage in firmware development involving developing new modules, re-architecting, and optimizing new and existing code for performance, taking advantage of hardware acceleration.
- Develop new tools and frameworks to support and automate the analyses.
- Conduct thorough analyses of sensor signal data, including SNR, noise characterization, and performance evaluation, to identify areas for improvement.
- Efficiently convert algorithms from Python/MATLAB into C, ensuring seamless integration and enhanced efficiency.
- Conduct performance testing and validation of signal processing algorithms in both simulation and real-world scenarios.
- Document all designs, methodologies, and results thoroughly for knowledge sharing and future reference.
QUALIFICATIONS:
- Bachelor's or Master’s degree in an engineering-related field with at least 2-years of industry experience working with signal processing and/or embedded systems
- Strong understanding of signal processing fundamentals and applications
- Proficiency in C and Python
- Experience with embedded systems and implementing signal processing or ML algorithms
- Creative problem-solver with a passion for innovating, improving processes, and developing new tools from scratch
PREFERRED QUALIFICATIONS:
- Knowledge of control theory as applied to sensor systems
- Familiarity with bare-metal or RTOS-based development on ARM-M series
- Strong understanding of how to map high-level algorithms to low-level implementation in C
- Experience with biosensor systems or biomedical signal processing, in particular with optics systems such as photodiodes, LEDs, and analog components such as ADCs
- Familiarity with Machine Learning algorithms and development
- Experience with statistical analysis and hypothesis testing
