WHOOP is an advanced health and fitness wearable, on a mission to unlock human performance. WHOOP empowers its members to improve their health and perform at a higher level by providing a deep understanding of their bodies and daily lives.
As a Machine Learning Engineer II on our Training team, you will develop algorithmic features and metrics that capture aspects of daily movement, exercise, and training. You'll integrate diverse data sources—including WHOOP sensor data and gold-standard reference datasets—while grounding your work in clinical theory and scientific literature.
You’ll work with data from a variety of sources including processed time series data generated by sensors on the WHOOP Strap, data collected from “gold-standard” devices, and data entered manually by WHOOP members via the mobile application. Using these data sources, as well as drawing upon clinical theory and evidence, you will design, train, and deploy machine learning algorithms to analyze training data. We’re looking for someone who has experience developing ML models with large datasets in Python and who is excited about the use of wearables in the health and wellness space.