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Sr. Software Engineer Machine Learning And Artificial Intelligence

One of the nation's largest and most respected providers of hospital and healthcare services, Universal Health Services, Inc. (NYSE: UHS) has built an impressive record of achievement and performance. Growing steadily since its inception into an esteemed Fortune 300 corporation, annual revenues were $15.8 billion in 2024. UHS was again recognized as one of the World's Most Admired Companies by Fortune; and listed in Forbes ranking of America's Largest Public Companies. Headquartered in King of Prussia, PA, UHS has approximately 99,000 employees and continues to grow through its subsidiaries. Operating acute care hospitals, behavioral health facilities, outpatient facilities and ambulatory care access points, an insurance offering, a physician network and various related services located all over the U.S. States, Washington, D.C., Puerto Rico and the United Kingdom. The Corporate Information Services Department is seeking a dynamic and talented Sr. Software Engineer Machine Learning and Artificial Intelligence. The Sr. Software Engineer Machine Learning and Artificial Intelligence plays a crucial role in developing AI-driven solutions that enhance patient care, streamline operations, and improve clinical decision-making. This position requires expertise in machine learning, deep learning, software engineering, and healthcare data management with emphasis on platform architecture. The engineer collaborates with data scientists, clinicians, and IT teams to design, implement, and optimize AI models that integrate seamlessly into healthcare workflows. Key Responsibilities:

Development and Design: Design, train, and deploy predictive analytics models for disease detection, patient risk assessment, and personalized treatment recommendations. Develop computer vision algorithms for medical imaging analysis, assisting in diagnostics and anomaly detection. Implement natural language processing (NLP) models for extracting insights from clinical notes, electronic health records (EHRs), and medical literature. Architect and develop scalable AI-driven applications that integrate with healthcare systems. Ensure data governance, compliance (HIPAA, GDPR), and security best practices. Optimize AI models for real-time processing and efficient deployment in cloud or on-premise environments. Design and maintain data pipelines for structured and unstructured healthcare data. Ensure data quality, preprocessing, and feature engineering for AI model training. Work with big data technologies to manage large-scale healthcare datasets. Ensure AI solutions comply with HIPAA, GDPR, and other healthcare regulations. Implement robust security measures to protect patient data and prevent unauthorized access. Develop explainable AI (XAI) models to enhance transparency and trust in AI-driven healthcare decisions. Work closely with clinicians, researchers, and administrators to define AI-driven solutions that improve patient outcomes. Communicate AI insights effectively to non-technical stakeholders, ensuring alignment with healthcare objectives. Maintenance and Support: Establish monitoring tools to track AI model performance and reliability. Provide technical support for AI-driven healthcare applications, troubleshooting issues and optimizing performance. Implement model retraining strategies to adapt AI solutions to evolving healthcare data and requirements. Ensure long-term sustainability of AI models through version control, documentation, and stakeholder feedback. Provide technical leadership, mentoring junior engineers and guiding AI strategy within the organization. Qualifications:

Position Requirements: Knowledge and Skills: Bachelor's or Master's degree in Computer Science, AI, or a related field. 5+ years of experience in ML and AI development, with a strong focus on healthcare interoperability and platform architecture. Machine Learning & AI Expertise: Strong proficiency in Python, TensorFlow, PyTorch, and other ML frameworks. Experience with natural language processing (NLP) and computer vision for healthcare applications. Knowledge of reinforcement learning and large language models (LLMs). Familiarity with AI observability and experimenting tools such as MLFlow. Platform Architecture & Scalability: Expertise in microservices architecture, ensuring modular and scalable AI solutions. Proficiency in containerization (Docker, Kubernetes) for efficient deployment. Experience with cloud platforms (AWS, Azure, GCP) for scalable AI integration. Strong understanding of high-performance computing for large-scale healthcare data processing. Ability to design interoperable systems that integrate with healthcare standards like FHIR, HL7, and EHR systems. Ability to build ML inference endpoints for model serving. Data Engineering & Security: Build and maintain data pipelines for structured and unstructured healthcare data. Ensure data governance, compliance (HIPAA, GDPR), and security best practices. Work with big data technologies to manage large-scale healthcare datasets. Experience working with data warehouse solutions such as Snowflake, Data Lake, or Oracle ADB. Software Engineering & System Integration: Develop modular, scalable AI-driven applications for healthcare analytics. Optimize AI models for real-time processing and efficient deployment. Implement CI/CD pipelines for continuous integration and deployment. Leadership & Collaboration: Provide technical leadership, mentoring junior engineers and guiding AI strategy. Work closely with clinicians, researchers, and administrators to define AI-driven solutions. Communicate AI insights effectively to non-technical stakeholders. This opportunity provides the following: Challenging and rewarding work environment Growth and development opportunities within UHS and its subsidiaries Competitive compensation Excellent medical, dental, vision, and prescription drug plan 401k plan with company match Generous paid time off Travel requirements: 5% US or International travel