Staff Applied Machine Learning Scientist
Core
Develop and optimize production-ready edge algorithms that transform wearable sensor data into real-time physiological insights for health and performance.
Role type
Staff Applied Machine Learning Scientist (Edge AI & Signal Processing)
Builds
Real-time physiological insights for wearable devices
Domain
Wearable technology, physiological modeling, edge AI
Deliverable
production ML models
Required skills
Deep learning, machine learning, signal processing, Python, algorithm optimization, experimental design, embedded systems development
Preferred skills
C/C++, physiological signal experience, resource-constrained optimization (via careerplan.io/jobs/23264cb1-d3cf-43e6-b05a-ec5f0fca56c6-staff-applied-machine-learning-scientist-at-whoop)
Technologies
Python, C/C++, deep learning frameworks, wearable sensor platforms
Responsibilities
Design advanced algorithms for physiological time-series and multimodal sensor data; optimize algorithms for embedded deployment balancing accuracy with power/latency constraints; analyze large-scale sensor datasets to identify performance gaps; define rigorous evaluation methodologies and validation frameworks; collaborate with cross-functional teams to translate algorithmic innovations into production capabilities; pursue high-impact technical initiatives from research through production deployment.
Seniority
Staff, hands-on IC with technical direction
