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Staff Applied Machine Learning Scientist

WHOOP

Skills

Required

  • Machine Learning
  • algorithms
  • deep learning / signal processing
  • real-world applications
  • Python
  • algorithm development
  • experimentation
  • data analysis
  • C/C++
  • embedded algorithm development
  • AI

Preferred

  • physiological signals
  • sensors / biomedical sensing
  • digital and statistical signal processing
  • classical signal processing techniques
  • time-series and
  • multimodal sensor data
  • optimizing algorithms

Join WHOOP, a leading performance optimization company, as a Staff Applied Machine Learning Scientist. In this role, you will develop and improve production-ready edge algorithms that transform sensor data into real-time physiological insights. You will work closely with a cross-functional team and have a direct impact on WHOOP's sensing capabilities and member experience. Enjoy a flexible vacation policy, competitive benefits, and a complimentary WHOOP membership.

Design advanced algorithms that combine signal processing, physiological modeling, machine learning, and deep learning for physiological time-series and multimodal sensor data.

Collaborate closely with cross-functional teams to translate algorithmic innovations into production-ready capabilities and member-facing features.

Responsibilities

  • Develop and continuously improve production-ready edge algorithms that transform sensor data into accurate, reliable, and real-time physiological insights.
  • Experience optimizing algorithms for resource-constrained embedded or edge environments, including tradeoffs across accuracy, power, memory, compute, and latency, is a plus
  • Experience designing rigorous experiments, defining performance metrics, performing error analysis, and translating findings into algorithm improvements
  • Demonstrated ability to develop robust models using large, noisy, real-world datasets and achieve strong generalization across diverse conditions
  • Deep technical expertise in modern machine learning and deep learning methods, particularly for time-series and multimodal sensor data
  • Experience taking algorithms through the full development lifecycle, from research and prototyping through validation, deployment, monitoring, and continuous improvement

Requirements

  • 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
  • Strong proficiency in Python for algorithm development, experimentation, and large-scale data analysis; experience with C/C++ and embedded algorithm development is highly desirable
  • Strong commitment to embracing and leveraging AI tools in day-to-day work while maintaining high standards for quality and rigor
  • Strong analytical and problem-solving skills, with the ability to navigate ambiguity and make sound technical decisions
  • Interested in the role, but don’t meet every qualification? We encourage you to still apply!

Preferred

  • Experience with physiological signals, wearable sensors, or biomedical sensing is highly desirable
  • Strong foundation in digital and statistical signal processing, with the ability to combine classical signal processing techniques with modern learning-based approaches

Pay

USD 170,000 - 230,000

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