Senior Sensor Intelligence Engineer; Embedded Algorithms

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. By combining advanced sensing, data science, and technology designed for continuous wear, WHOOP delivers insights that help members better understand and improve their health and performance.

WHOOP is looking for a Senior Sensor Intelligence Engineer to join our Embedded Controls team within Sensor Intelligence and help shape the sensing capabilities across current and future WHOOP devices. Sitting at the intersection of signal processing, machine learning, embedded systems, and sensor technology, you will develop algorithms that interpret real-world sensor signals and intelligently control how sensors operate on-device. You will work across the sensing stack from understanding sensor physics and signal characteristics through algorithm development and embedded deployment to enable increasingly sophisticated sensing capabilities while meeting the power, compute, memory, and reliability constraints of a continuously worn device.

• Develop signal-processing and machine-learning algorithms that interpret physiological, motion, and other wearable sensor signals, including applications such as activity and state detection, wear detection, signal-quality assessment, and device-state estimation
• Design intelligent embedded control strategies that dynamically configure sensor behavior based on signal quality, device state, user context, system requirements, and power constraints
• Characterize new and existing sensors by understanding signal behavior, noise sources, artifacts, dynamic range, sampling requirements, calibration, operating modes, and the impact of analog and acquisition architectures
• Prototype algorithms and sensing strategies using tools such as Python or MATLAB, then partner with Firmware Engineering to translate them into robust, computationally efficient implementations suitable for resource-constrained embedded systems
• Partner closely with Firmware, Electrical Engineering, Data Science, Hardware, and broader Sensor Intelligence teams to bring up new sensing modalities, debug issues across the sensing stack, and inform sensing architectures for future WHOOP products
• Develop experiments, analysis frameworks, and validation methodologies to evaluate sensor and algorithm performance across users and real-world conditions, optimizing solutions for accuracy, robustness, latency, memory, compute, and power

• BS, MS, or PhD in Electrical Engineering, Computer Engineering, Biomedical Engineering, Computer Science, Applied Physics, or a related technical field, or equivalent practical experience
• Strong foundation in digital signal processing and time-series analysis, with experience applying techniques such as filtering, spectral analysis, sampling theory, noise reduction, and feature extraction to real-world sensor data
• Experience developing signal-processing and/or machine-learning algorithms for noisy, artifact-prone sensor data and taking algorithms beyond offline analysis toward real-time, embedded, or production implementation
• Strong proficiency in Python, MATLAB, or similar algorithm-development environments, with working knowledge of C/C++ and embedded-system considerations such as timing, memory, compute, and hardware interfaces
• Working knowledge of sensor and electrical systems, including concepts such as ADCs, analog front ends, sampling, digital interfaces, noise, calibration, and signal acquisition
• Ability to investigate and solve ambiguous engineering problems across algorithm, firmware, sensor, and electrical boundaries using strong experimental and analytical methods
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