Backed by SoftBank VF and IVP.
About the role
Design and develop algorithms that combine 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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, including optimization across accuracy, power, memory, compute, and latency
More about this role
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. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep.
We are 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. Working closely with Data Science, Firmware, Hardware, and domain experts, you will deliver robust on-device algorithms that improve the member experience while meeting the power, memory, compute, and latency constraints of wearable hardware.
Design and develop algorithms that combine signal processing, feature extraction, and machine learning to derive meaningful physiological insights from wearable sensor...
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