Build robust IMU processing pipelines, including filtering, calibration, time synchronization, resampling, coordinate transformations, and compensation for sensor noise and systematic errors.
Analyze large-scale, noisy wearable datasets to identify failure modes and improve robustness across motion patterns, device orientation, fit, and individual movement characteristics.
Translate algorithm prototypes into production-ready C/C++ implementations, balancing accuracy with power, memory, compute, numerical stability, and latency.
Partner with Firmware and Hardware teams on sensor configuration, sampling strategies, and on-device integration; investigate differences between prototype behavior and performance on target hardware.
Define performance metrics and rigorous validation plans using reference measurements, controlled experiments, real-world data, and on-device testing to assess accuracy, robustness, and long-duration stability.
Own technical decisions for complex algorithm components, contribute to design and code reviews, mentor teammates, and collaborate across functions to investigate post-deployment issues and drive continuous improvement.
QUALIFICATIONS
5+ years of experience developing and deploying signal processing, sensor fusion, or state-estimation algorithms for real-world sensor applications, including substantial hands-on work with IMU data.
MS or PhD in Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics, or a related quantitative field, or equivalent practical experience.
Strong foundation in digital and statistical signal processing, including filtering, spectral analysis, stochastic processes, and sensor noise characterization.
Demonstrated experience with accelerometer and gyroscope processing, IMU calibration, sensor error modeling, bias estimation, and drift management.
Strong experience with Kalman filtering, including extended or error-state formulations, and model-based sensor fusion; practical understanding of nonlinear estimation, uncertainty propagation, and estimator failure modes.
Practical knowledge of 3D motion and orientation representations, including coordinate frames, rotation matrices, quaternions, and rigid-body kinematics.
Proficiency in Python for algorithm development, data analysis, and experimentation, and C/C++ for efficient embedded implementation; demonstrated experience deploying algorithms to resource-constrained hardware.
Ability to independently investigate complex algorithmic problems, design rigorous validation experiments, and communicate technical tradeoffs with firmware, hardware, and cross-functional partners.
Experience with body-worn sensors, human-motion modeling, motion segmentation, or periodicity analysis is highly desirable.
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.
Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.
WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
