Design and develop scalable data generation pipelines that prioritize robustness, reproducibility, and quality across diverse physiological signals.
Research, prototype, and implement novel ML architectures for large-scale time-series modeling of health and physiological data.
Build scalable training pipelines to support high-throughput model development and iteration.
Design comprehensive evaluation strategies for model performance and clinical validity.
Collaborate cross-functionally with engineering, product, and validation teams to bring scientific models to production.
Plan and support long-term roadmap and mentor junior team members on best practices in modeling and deployment.
Requirements
Have a PhD in machine learning, artificial intelligence, biomedical engineering, or a closely related field.
Bring 5+ years of relevant industry experience (post-PhD) working with applied ML in a product setting.
Have hands-on experience developing and deploying large time-series models, especially those built on sequential physiological or wearable data.
Demonstrate strong programming skills in Python and experience with cloud-based ML (e.g., AWS, Github, Pytorch, Docker).
Possess a deep understanding of scalable ML workflows, including data pipelines, evaluation frameworks, and deployment to production.
Self-starter and vision-driven with strong collaboration and communication skills and thrive in cross-functional settings.
(Bonus) Have a background in physiology, health sensing, or digital biomarkers.
(Bonus) Have experience shipping ML models in production environments and conducting real-world validation.
Benefits
Competitive salary and equity packages
Health, dental, vision insurance, and mental health resources
An Oura Ring of your own plus employee discounts for friends & family
20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
Paid sick leave and parental leave
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningartificial intelligencedata generation pipelinestime-series modelingmodel evaluationmodel deploymentprogramming in Pythonscalable ML workflowslarge time-series modelsphysiological data