Staff Applied Machine Learning Scientist

WHOOP; Boston (MA); On-site; USD 170,000 - 230,000; Full time

Job summary

WHOOP is seeking a Staff Applied Machine Learning Scientist in Boston to develop edge algorithms that transform wearable sensor data into real-time physiological insights. You will combine deep learning, ML, and signal processing to optimize accuracy, latency, power, and scalability across wearable platforms.

You will work with a cross-functional team to move novel modeling approaches from research to production and ongoing improvement, shaping WHOOP's sensing capabilities and member insights.

Qualifications

• MS or PhD in Electrical Engineering, Biomedical Engineering, CS, ML, or related quantitative field.
• 7+ years of experience developing and deploying machine learning, deep learning, and/or signal processing algorithms for real-world applications.
• Strong proficiency in Python; experience with C/C++ and embedded algorithm development is desirable.
• Experience with time-series and multimodal sensor data.

Responsibilities

• Design advanced algorithms that combine signal processing, physiological modeling, machine learning, and deep learning for physiological time-series and multimodal sensor data.
• 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 and drive data-informed algorithm improvements.
• Define evaluation methodologies, validation frameworks, and performance metrics for development and deployment.
• Optimize algorithms for embedded deployment, balancing accuracy with power, memory, latency, and compute constraints across wearable platforms.
• Set technical direction and best practices for modeling, experimentation, and validation across sensing problems.
• Pursue ambitious technical initiatives from research through deployment and continuous improvement.
• Collaborate with Data Science, Firmware, Software, Hardware, and Product teams to translate innovations into production capabilities.
• Stay at the forefront of advances in deep learning, signal processing, edge AI, and physiological sensing.

Skills

Python

Deep learning

Time-series

Edge AI

Signal processing

C/C++

Embedded

Experiment design

Multimodal data

Education

MS/PhD in Electrical Engineering, Biomedical Engineering, CS, ML, or related field

Job 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 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.

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! As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.

WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Compensation Range: $170K - $230K