Staff Machine Learning Engineer at Tebra
Interview Preparation Plan
As a Staff Machine Learning Engineer at Tebra, you will be instrumental in designing, training, and operating state-of-the-art machine learning systems that power Tebra's platform. This hands-on technical leadership role involves owning the entire ML lifecycle, from initial data exploration and model development to production deployment, ongoing monitoring, and continuous improvement. You will be at the forefront of applying ML in the healthcare industry, transforming complex data into reliable automation that drives significant business impact, such as improving efficiency and enabling automation. Your work will directly contribute to modernizing patient care and optimizing healthcare operations through advanced AI capabilities. Tebra is a comprehensive technology platform focused on streamlining operations for independent healthcare practices. By integrating EHR, revenue cycle management, and patient scheduling, Tebra aims to optimize practice efficiency and support the connected practice of the future. This role offers the opportunity to push the boundaries of applied ML within a regulated healthcare environment, establishing best practices for model governance, reproducibility, explainability, and observability. You will also play a key role in mentoring other engineers in applied ML methods and system design.
Key Responsibilities
- Design, build, and operate scalable ML pipelines for data ingestion, feature generation, model training, evaluation, deployment, and monitoring.
- Own the end-to-end ML lifecycle, including data exploration, feature engineering, model design, validation, and productionization.
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