About the Role

As a Staff Machine Learning Engineer, you’ll design, train, and operate best-in-class machine learning systems that power our Tebra platform. You’ll own the entire lifecycle — from data exploration and model development to production deployment, monitoring, and continuous improvement.

This is a hands-on technical leadership role where you’ll push the boundaries of applied ML in healthcare, transforming messy real-world data into reliable automation that drives measurable business impact.

Your Area of Focus

• 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.

Your Professional Qualifications

• 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 mo