Oura is looking to bridge cutting-edge research and real-world health applications by analyzing large-scale research and clinical datasets, developing and validating deep learning models for wearable sensor data, and collaborating with cross-functional teams to translate findings into production features and scientific publications. The goal is to improve health outcomes for Oura members and advance scientific understanding of human physiology through wearable data.
Requirements
- Hands-on experience developing and evaluating deep learning models for time-series or sensor data, ideally involving physiological or wearable signals.
- Strong proficiency in Python and core ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Solid foundation in probability, statistics, and experimental design, with experience interpreting results from large-scale research or clinical studies.
Responsibilities
- Analyze research and clinical datasets to evaluate algorithm performance and uncover new physiological and health insights.
- Collaborate with scientists, clinicians, engineers, and product teams to translate research advances into production features that bring value to Oura members.
- Drive scientific publications and validation studies that advance understanding of human physiology and support Oura’s mission to improve health.
- Research, prototype, and implement deep learning models for large-scale physiological time-series data (e.g., PPG, motion, temperature), including foundation model approaches.
- Build and optimize scalable data generation, training and evaluation pipelines for physiological modeling.
Other
- A PhD or MSc with 3+ years of industry experience, in biomedical engineering, machine learning, or a related field. Experience in healthcare or health technology is preferred.
- Excellent scientific writing skills and a record of first-author, peer-reviewed publications.
- Collaborative mindset, ability to work autonomously, and comfort operating in an interdisciplinary, globally distributed team.
- Flexibility to adjust schedules for occasional meetings across time zones with European colleagues, and willingness to travel for onsite meetings a few times per year.
- This is a remote US role with a strong preference for candidates based on the East coast.
