Staff Machine Learning Engineer

Tebra is a company focused on improving healthcare by modernizing care for independent practices. The Staff Machine Learning Engineer will design, train, and operate machine learning systems, owning the entire lifecycle from data exploration to production deployment.

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
• Lead and mentor engineers in applied ML methods, system design, and data-driven experimentation

Requirements:

• 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
• 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
• Experience working with structured business or healthcare data is a plus
• Excellent technical communication and a product mindset — comfortable driving initiatives from concept to delivery
• Background in healthcare software operations, or financial automation
• Contributions to open-source ML infrastructure projects
• Published research or conference papers in machine learning, natural language processing, or applied AI
• Experience leading AI reliability and observability initiatives — designing monitoring frameworks, drift detection, and alerting systems for multiple production models