# Staff Applied Machine Learning Scientist at Whoop

AI Level 4, AI centrality 90 out of 100. Boston, MA.

## Details

- Company: [Whoop](https://jobsbylevel.com/companies/whoop)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Boston, MA
- Posted: October 6, 2026
- Apply: https://jobsbylevel.com/go/66f9d020-d1de-4f56-ae81-510a769e7011

## Description

At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep. We are seeking a Staff Applied Machine Learning Scientist to develop and continuously improve production-ready edge algorithms that transform sensor data into accurate, reliable, and real-time physiological insights. In this role, you will develop innovative approaches that combine deep learning, machine learning, and signal processing to deliver meaningful value to WHOOP members while optimizing for accuracy, latency, power, and scalability across wearable platforms. You will work closely with a cross-functional team of scientists, engineers, physiologists, and hardware experts to solve challenging problems at the intersection of wearable sensing, physiological modeling, and applied AI. You will develop novel modeling approaches, and own complex algorithmic problems from early development through production deployment and continuous improvement. Your work will directly shape the future of WHOOP’s sensing capabilities and our ability to provide members with accurate, personalized, and actionable insights into their health and performance. 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

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