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This listing was closed on Jul 22, 2026.
Browse Open JobsSenior Machine Learning Research Engineer (Deep Learning, Sensor Intelligence Group)
Summary by AIEngineer.careers
Senior Machine Learning Research Engineer (Deep Learning, Sensor Intelligence Group) is a AI Research Engineer role (full-time). with WHOOP. in BOSTON, US. Compensation shown: $190K–$225K. Imported listing (source: sportstechjobs.com). Apply on the employer's site (sportstechjobs.com).
Imported listing (source: sportstechjobs.com) · Apply on sportstechjobs.com
BOSTON · US
$190K–$225K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from sportstechjobs.com
WHOOP is seeking a Senior ML Research Engineer to join the Sensor Intelligence Group, working on member-facing and regulated health features. The role involves designing and training deep-learning models on biosensor time-series data and deploying on edge and cloud.
Responsibilities
- Design and train deep-learning and machine-learning models for biosensor data; stay updated with DL research; support documentation for regulated health features; write clean, maintainable code; monitor algorithm performance; conduct experiments and testing; optimize models for production deployment; prepare reports; contribute to research; collaborate with cross-functional teams.
Requirements
- Master's or PhD in CS, EE, Biomedical Engineering, Data Science, AI, or related field; published research in ML/DL; 4+ years of ML/DL research experience (2+ post-PhD); strong time-series and signal processing skills; solid DL architecture knowledge; proficiency in Python and DL frameworks (PyTorch, TensorFlow).
Nice to Have
- Experience with regulated or high-risk ML systems (digital health, medical devices); training/fine-tuning/deploying Foundation AI models; cloud computing platforms (AWS or GCP).
WHOOP
Boston · US · 1175+ employees
WHOOP provides a wearable fitness tracker that monitors biometric data to deliver personalized health and performance insights. It is widely used by athletes for recovery and sleep tracking.
