Staff Applied Machine Learning Scientist

Responsibilities

• Design advanced algorithms that combine signal processing, physiological modeling, machine learning, and deep learning for physiological time-series and multimodal sensor data, with a focus on accuracy, robustness, and generalization across diverse members and real-world conditions.
• Own the development and continuous improvement of production-ready edge algorithms that transform multimodal sensor data into accurate, reliable, and real-time physiological insights.
• Analyze large-scale wearable sensor datasets to identify performance gaps, characterize challenging conditions, and drive data-informed algorithm improvements.
• Define rigorous evaluation methodologies, validation frameworks, and performance metrics to assess algorithms throughout development and deployment.
• Optimize algorithms for embedded deployment, balancing accuracy with power, memory, latency, and compute constraints across current and future wearable platforms.
• Set technical direction and establish best practices for modeling, experimentation, validation, and algorithm development across complex sensing problems.
• Pursue ambiguous, high-impact technical initiatives from research and prototyping through validation, production deployment, monitoring, and continuous improvement.
• Collaborate closely with Data Science, Firmware, Software, Hardware, Product, and domain experts to translate algorithmic innovations into production-ready capabilities and member-facing features.
• Stay at the forefront of advances in deep learning, machine learning, signal processing, edge AI, and physiological sensing, and translate relevant innovations into differentiated WHOOP capabilities.

Qualifications

• MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, Machine Learning, Applied Mathematics, or a related quantitative field.
• 7+ years of experience developing and deploying machine learning, deep learning, and/or signal processing algorithms for complex real-world applications.
• Deep technical expertise in modern machine learning and deep learning methods, particularly for time-series and multimodal sensor data.
• Strong foundation in digital and statistical signal processing, with the ability to combine classical signal processing techniques with modern learning-based approaches.
• Strong proficiency in Python for algorithm development, experimentation, and large-scale data analysis; experience with C/C++ and embedded algorithm development is highly desirable.
• Demonstrated ability to develop robust models using large, noisy, real-world datasets and achieve strong generalization across diverse conditions.
• Experience designing rigorous experiments, defining performance metrics, performing error analysis, and translating findings into algorithm improvements.
• Experience taking algorithms through the full development lifecycle, from research and prototyping through validation, deployment, monitoring, and continuous improvement.
• Strong analytical and problem-solving skills, with the ability to navigate ambiguity and make sound technical decisions.
• Experience with physiological signals, wearable sensors, or biomedical sensing is highly desirable.
• Experience optimizing algorithms for resource-constrained embedded or edge environments, including tradeoffs across accuracy, power, memory, compute, and latency, is a plus.
• Strong commitment to embracing and leveraging AI tools in day-to-day work while maintaining high standards for quality and rigor.

Additional information

Join us in pushing the boundaries of wearable sensing, applied AI, and physiological intelligence 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.

Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.