Oura is looking to develop the next generation of health navigation tools by building robust, scalable, and accurate behavioral ML/AI models from large-scale behavioral, physiological, and health datasets.
Requirements
- Hands-on experience developing and deploying large time-series models on sequential physiological/wearable, health, or behavioral data.
- Strong programming skills in Python and familiarity with cloud-based ML tools (e.g., AWS, GitHub, PyTorch, Docker).
- Deep understanding of experimental design and model/outcome evaluation methodologies.
- (Bonus) Experience shipping ML models in production and conducting real-world validation.
Responsibilities
- Build scalable data-generation pipelines that prioritize robustness, reproducibility, and quality across diverse behavioral, physiological, and health signals.
- Research, prototype, and implement novel ML architectures for large-scale time-series modeling of behavioral, physiological, and health data.
- Develop and maintain high-throughput training pipelines to accelerate model iteration.
- Design comprehensive evaluation strategies for model performance and clinical validity.
- Partner with engineering, product, and design teams to productionize models and communicate complex health information to Oura members.
- Help plan our medium- and long-term R&D roadmap, balancing innovation with iterative product delivery.
Other
- Have a PhD in biomedical engineering, electrical engineering, biostatistics, computer science or a related field, OR a Master degree in related fields with 3+ years of relevant work experience.
- 1–3 years of industry experience in applied ML/AI within product and/or clinical settings.
- Self-starter with a vision-driven mindset.
- Excellent collaboration and communication skills; thrive in cross-functional teams.
- (Bonus) Background or experience in behavioral science or behavior-driven health outcome research/clinical practice.
