Turn data science models for glucose forecasting, pattern detection, and personalization into reliable, scalable cloud services. Build real-time and batch inference systems and feature pipelines using sensor and user-context data, and collaborate with data science, platform, and product teams.
Responsibilities
- Productionize time-series and personalization models into scalable, monitored services.
- Refactor and optimize data science code for performance, reliability, and maintainability.
- Build batch and real-time inference services to meet latency, cost, and availability targets.
- Expose model outputs as reliable tools and signals for agentic AI systems.
- Build and maintain feature pipelines using sensor, meal, activity, and user-context data.
- Address sensor data quality challenges and contribute to design reviews, verification, and technical documentation.
Required Skills
- Python
- Py
- Torch
- Tensor
- Flowscikit-learn
- Time-series modeling
- Forecasting
- Cloud platforms
- Containerization
- ML pipelines
- CI/CD
