Senior Applied ML Algorithm Engineer

About the Role

WHOOP is 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.

Responsibilities

• Design and develop algorithms combining 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.
• Collaborate with Data Science, Software, Product, and domain experts to translate research findings into production-ready capabilities and investigate post-deployment performance gaps.

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, optimizing across accuracy, power, memory, compute, and latency.
• Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs.
• Strong commitment to embracing and leveraging AI tools in day-to-day tasks.