Senior Sensor Algorithm Engineer

MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, or a related field.
5+ years of experience developing signal processing and machine learning algorithms for time-series data.
Proficiency in Python and C/C++ for algorithm development and data analysis.
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and signal processing techniques.
Deep understanding of physiological signal processing and time-series modeling.
Strong analytical and problem-solving skills with the ability to work on complex, ambiguous problems.
Experience deploying algorithms in production environments, including embedded systems or cloud-based solutions, is a plus.
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.

Develop and optimize signal processing and machine learning algorithms to extract physiological metrics from raw sensor data.
Analyze large-scale wearable sensor datasets to improve accuracy, robustness, and efficiency of existing and new algorithms.
Collaborate with data scientists, engineers, and domain experts to translate research findings into production-ready features.
Implement scalable and efficient algorithms for real-time and offline processing of physiological signals.
Work closely with firmware and hardware teams to ensure seamless integration of algorithms on WHOOP devices.
Stay up to date with advancements in machine learning, signal processing, and physiological modeling, bringing innovative ideas to WHOOP.
Validate algorithms through rigorous testing, including lab-based experiments and real-world data analysis.

Experience deploying algorithms in production environments, including embedded systems or cloud-based solutions, is a plus.