Tebra is looking to solve the problem of transforming messy real-world data into reliable automation that drives measurable business impact in the healthcare industry
Requirements
- Proficiency in Python, TensorFlow/PyTorch, and scikit-learn
- Hands-on experience with data analysis, feature engineering, and model development on large, complex datasets
- Strong background in MLOps and data infrastructure (e.g., Airflow, Spark, feature stores, MLflow, data versioning)
- Proven ability to deploy and maintain ML models in production with CI/CD, monitoring, and alerting
- Familiarity with cloud ML environments (AWS, GCP, or Azure) and containerization (Kubernetes, Docker)
- Experience building or fine-tuning LLMs or generative models for structured business processes
- Experience with retrieval-augmented pipelines or feedback-driven model retraining
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
- Continuously monitor model performance in production, detect drift, and implement automated retraining pipelines to ensure accuracy and reliability over time
- Leverage advanced ML techniques — from gradient boosting to large language models — to improve automation and prediction across claims, payments, and billing workflows
- Conduct in-depth data analysis and experimentation to identify new opportunities for model-driven efficiency
- Collaborate cross-functionally with engineering, product, and data teams to integrate AI capabilities directly into Tebra’s platform
- Establish best practices for model governance, reproducibility, explainability, and observability within regulated healthcare environments
Other
- 8+ years of professional software engineering experience, including system design, large-scale services, and production-grade infrastructure
- 5+ years of hands-on experience in machine learning engineering or applied AI, with a strong record of deploying and maintaining models in production
- Demonstrated ability to deliver significant, measurable real-world impact through applied ML — improving efficiency, automation, or business outcomes
- Excellent technical communication and a product mindset — comfortable driving initiatives from concept to delivery
- Background in healthcare software operations, or financial automation is a plus
