Staff Machine Learning Engineer

📋 Description

• • Lead end-to-end machine learning initiatives from problem identification to production deployment and continuous iteration for healthcare operations automation.
• • Design, develop, and optimize ML models and AI systems focused on document parsing, extraction, classification, and intelligent automation of provider credentialing and verification workflows.
• • Build and maintain robust, scalable, and observable production ML pipelines that handle high-volume healthcare data with reliability and performance.
• • Integrate and fine-tune third-party AI services including OpenAI, Amazon Textract, and cloud-based ML APIs, managing tradeoffs between cost, latency, and model accuracy.
• • Analyze structured and unstructured datasets to uncover patterns, validate model performance, and generate actionable insights that drive product and operational improvements.
• • Shape the company’s AI roadmap by evaluating technical feasibility, product impact, and strategic alignment across engineering and product teams.
• • Drive architectural decisions for ML systems, establishing best practices for model development, evaluation, monitoring, and deployment.
• • Mentor and guide junior and mid-level engineers, modeling high standards for ML software engineering, code quality, and system design.
• • Collaborate cross-functionally with product managers, designers, and operations teams to define requirements, prioritize initiatives, and deliver solutions that reduce administrative burden in healthcare.
• • Ensure all ML systems comply with regulatory and compliance standards inherent to healthcare data handling and provider network management.
• • Continuously evaluate emerging AI technologies and methodologies to maintain Medallion’s competitive edge in automated provider credentialing and compliance monitoring.
• • Translate complex business problems in healthcare operations into well-defined machine learning problems with measurable success criteria.
• • Own the end-to-end lifecycle of ML models in production, including data ingestion, feature engineering, training, validation, serving, and retraining workflows.
• • Communicate technical concepts and model outcomes clearly to both technical and non-technical stakeholders across the organization.
• • Balance innovation with operational stability, ensuring that AI systems are both cutting-edge and reliable in a regulated healthcare environment.
• • Contribute to the evolution of Medallion’s AI engineering culture through documentation, code reviews, and knowledge sharing across the engineering organization.