“RESPONSIBILITIES:
- Design, optimize and maintain machine learning algorithm on the edge device
- Collaborate closely with Data Science, Firmware, and Research teams to enhance user metrics by developing innovative and efficient algorithms that are deployable on low-power embedded systems.
- Design, prototype, and implement machine learning solutions that run on edge devices with limited compute, memory, and power budgets.
- Participate in the full software development lifecycle, including development, debugging, hardware-in-the-loop testing, and deployment on edge platforms.
- Leverage expertise in signal processing, time-series analysis, and embedded ML to optimize biosensor systems and improve inference accuracy at the edge.
- Explore, model, and implement algorithms that balance performance and power efficiency while maintaining scalability and adaptability.
- Contribute to research efforts exploring new features, hardware-aware model optimization, and intelligent data processing pipelines for edge deployment.
QUALIFICATIONS:
- Bachelor’s or Master’s degree in applied mathematics, electrical/biomedical engineering, computer engineering, or a related field.
- 2+ years of industry or research experience in signal processing and/or machine learning, preferably with deployment experience on embedded or wearable platforms.
- Understanding of biosensor systems and analysis of physiological signals in noisy, real-world conditions.
- Strong programming proficiency in C and/or Python
- Experience developing and optimizing machine learning models for edge devices including model quantization, pruning, or lightweight inference.
- Working knowledge of adaptive signal processing, real-time systems, and time-series analysis.
- Deep understanding of ML libraries such as TensorFlow Lite, scikit-learn, PyTorch, or TinyML frameworks.
- Excellent communication skills, both written and oral, with a track record of conveying complex technical topics to diverse teams.
- Demonstrated creativity, adaptability, and a passion for building impactful products that scale to real-world, edge-deployable use cases.
Join us in pushing the boundaries of wearable technology and positively impacting people’s lives!
This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. ”
