Director, Software Engineering - Remote

UnitedHealth Group is a global leader in health care innovation, seeking a Director of Software Engineering to lead the Individual & Family Plans Technology team. This role focuses on redefining health benefits delivery through advanced technology solutions, including AI and ML, while fostering a high-performing engineering culture.

Responsibilities:

• Own end-to-end engineering delivery across the Individual & Family Plans (IFP) portfolios, driving strategic roadmaps, backlog prioritization, and execution predictability
• Lead cross-application delivery, integration, and dependency management while ensuring high-quality design, engineering standards, and robust governance
• Lead the development and execution of the AI/ML strategy for the portfolio, strategically using artificial intelligence to solve complex business problems, unlock new opportunities, and deliver tangible business value
• Translate AI investments into production-grade, measurable, and scalable outcomes, preventing fragmented experimentation by driving enterprise-aligned, robust solutions
• Champion AI as a core driver of the engineering team's success, proactively shaping how AI technologies are developed, deployed, and utilized
• Integrate enterprise AI tools, establish AI-driven development goals within team growth plans, and align hiring with necessary AI competencies while actively removing barriers to accelerate adoption
• Champion the ethical and responsible use of AI by embedding transparency, fairness, security, and accountability throughout the entire AI development and model lifecycle
• Partner closely with data science, architecture, and data teams for scalable AI integration, as well as business leaders and external stakeholders, serving as the primary IT interface for business, state, and federal interactions where applicable
• Influence overall business strategy, prioritization, and investment decisions through articulate and compelling business communications
• Cultivate a compassionate, supportive, and high-performing engineering culture that values collaboration, professional growth, and alignment with the enterprise mission

Requirements:

• Bachelor's degree or 4+ years of equivalent software engineering experience
• 10+ years of professional software engineering experience, with 5+ years in a formal leadership/management role guiding large-scale, distributed engineering teams
• 3+ years of experience leading the integration and delivery of AI/ML or data-driven capabilities into complex enterprise platforms
• Experience working in a regulated environment (healthcare preferred) with hands-on exposure to compliance, governance, and responsible/ethical technology deployment
• Demonstrated experience with cloud architecture and distributed systems supporting large-scale workloads (e.g., model deployment, data pipelines, scalable inference)
• Solid proficiency with modern engineering practices (Agile, DevOps, CI/CD) and MLOps practices (e.g., model lifecycle management, monitoring, continuous integration/delivery of models)
• Experience collaborating directly with data science, product, and business teams to drive strategic alignment and translate technology investments into measurable business outcomes
• Highly articulate communication skills, with a proven track record of influencing strategy, investments, and prioritization decisions with senior business leaders and external stakeholders
• Experience in the healthcare industry, specifically dealing with Individual & Family Plans (IFP) or consumer health insurance platforms
• Experience establishing development goals focused on accelerating enterprise AI tool adoption and technical skill building
• Expertise in evaluating AI model performance, business impact, and ensuring responsible AI guardrails are embedded in production systems
• Passion for compassionate leadership, active mentorship, and building psychologically safe, high-performing engineering cultures
• Deep knowledge of scalable data pipelines, feature engineering, and model inference at scale in a cloud environment (e.g., AWS, Azure, GCP)