Senior Sensor Algorithm Engineer
Overview:
In this role you will design and deploy real-time sensor algorithms for wearable devices, focusing on optical and IMU data to deliver accurate, on-device insights. You'll work across firmware, hardware, data science, and product to ensure robust performance in diverse conditions. You will bridge prototype algorithms to production, balancing accuracy with hardware constraints, and drive continuous improvement through validation and experimentation. This is a chance to shape how WHOOP translates physiological signals into actionable performance insights.
Responsibilities:
- Design and develop real-time signal processing and sensor fusion algorithms to characterize motion and deliver member-facing insights
- Develop and tune model-based estimators for orientation and motion, including bias estimation and drift mitigation
- Build robust IMU processing pipelines (filtering, calibration, time synchronization, resampling, coordinate transformations)
- Analyze large-scale wearable datasets to identify failure modes and improve robustness across motion patterns and device orientation
- Translate prototypes into production-ready C/C++ implementations with attention to power, memory, compute, numerical stability, and latency
- Collaborate with Firmware and Hardware on sensor configuration and on-device integration; study prototype vs target hardware performance
- Define performance metrics and validation plans using controlled experiments, real-world data, and on-device testing
- Own technical decisions for complex algorithm components, review code, mentor teammates, and drive continuous improvement
Key requirements:
- 5+ years in signal processing, sensor fusion, or state-estimation for real-world sensors with IMU experience
- Strong foundation in digital and statistical signal processing (filters, spectral analysis, stochastic processes, sensor noise)
- Experience with accelerometer and gyroscope processing, IMU calibration, sensor error modeling, bias estimation, drift management
- Proficiency in Kalman filtering (extended or error-state) and model-based sensor fusion; understanding nonlinear estimation and uncertainty propagation
- Knowledge of 3D motion/orientation representations (rotation matrices, quaternions, rigid-body kinematics)
- Proficiency in Python for development and experimentation, and C/C++ for embedded implementation; experience deploying to resource-constrained hardware
- Ability to investigate complex algorithmic problems, design validation experiments, and communicate tradeoffs with cross-functional teams
- Experience with body-worn sensors, human-motion modeling, motion segmentation, or periodicity analysis (desirable)collaboration with cross-functional teamsstrong communication of technical tradeoffsproblem-solving mindset
- Kalman filtering (extended/error-state)sensor fusionIMU data processing and calibration