Senior Applied ML Algorithm Engineer

Job title: Senior Applied ML Algorithm Engineer

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 Senior Applied ML Algorithm Engineer to develop and deploy signal processing algorithms and machine learning models that transform raw sensor data into accurate, reliable, and real-time physiological insights on WHOOP devices. In this hands-on role, you will own algorithm development from data analysis, model training, and Python prototyping through efficient C/C++ implementation, firmware integration, and production validation. Working closely with Data Science, Firmware, Hardware, and domain experts, you will deliver robust on-device algorithms that improve the member experience while meeting the power, memory, compute, and latency constraints of wearable hardware.

RESPONSIBILITIES

• Design and develop algorithms that combine signal processing, feature extraction, and machine learning to derive meaningful physiological insights from wearable sensor data.
• Analyze large-scale, noisy sensor datasets to train and evaluate models, identify performance gaps, and improve accuracy, robustness, and generalization across diverse members and real-world conditions.
• Translate algorithm prototypes and trained models into production-ready C/C++ implementations, optimizing signal processing pipelines and on-device inference for accuracy, power, memory, compute, and latency.
• Partner closely with Firmware and Hardware teams to integrate algorithms into production firmware, validate execution on target hardware, and resolve differences between prototype and embedded performance.
• Define performance metrics and rigorous validation plans, combining offline evaluation, lab-based experiments, real-world data analysis, and on-device testing to assess algorithm accuracy, reliability, and efficiency.
• Collaborate with Data Science, Software, Product, and domain experts to translate research findings into production-ready capabilities, investigate post-deployment performance gaps, and drive continuous algorithm improvement.

QUALIFICATIONS

• 5+ years of experience developing signal processing and machine learning algorithms for time-series or sensor data in real-world applications.

• MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or a related quantitative field, or equivalent practical experience.

• Strong foundation in digital and statistical signal processing for noisy time-series data. Experience with physiological signals or wearable sensors is a plus.

• Proficiency in Python for data analysis, model development, and experimentation, and C/C++ for implementing efficient algorithms in embedded firmware.

• Experience developing, training, and evaluating machine learning models using frameworks such as TensorFlow, PyTorch, or scikit-learn.

• Demonstrated experience deploying machine learning models to resource-constrained embedded systems, including optimization across accuracy, power, memory, compute, and latency.

• Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs with firmware, hardware, and data science partners.

• Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.

• 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.