Director, Engineering (Patient)

The Problems You'll Solve: - Build an ML-powered matching system from the ground up to replace current filter-based systems, incorporating provider communication style, expertise signals, and patient outcome data. - Rearchitect team topology as the product evolves, managing the transition of ownership boundaries between ranking, activation, and onboarding pods. - Scale the organization from approximately 18 to 25+ engineers while maintaining velocity and quality. - Establish an operating model where engineering, product, and data science jointly co-own patient funnel metrics (intake-to-match, match-to-book, book-to-retained). - Set concrete standards for AI-assisted development and define the AI/ML product strategy regarding model usage, explainability, and building patient trust. Key Responsibilities: - Set engineering strategy for the core patient experience in partnership with Product and Data Science leadership. - Lead 3 Engineering Managers; scale the org from approximately 18 to 30+ engineers over the next 18 months. - Co-own patient funnel metrics with Product and Data counterparts. - Drive delivery of ML-powered ranking, reimagined patient onboarding, and patient activation systems. - Build an engineering culture with clear standards for velocity, quality, and technical excellence. Required Experience: - 10+ years of software engineering experience, 5+ years managing engineering managers. - Led engineering for a consumer or marketplace product where search, matching, ranking, or personalization was core to the business. - Scaled an engineering organization through a high-growth phase (25+ to 50+) while maintaining velocity and quality. - Technically strong in architecture calls on ranking/ML systems, marketplace infrastructure, and consumer-facing surfaces. - Experience shipping ML-powered product features at consumer scale. - Track record of moving business metrics through engineering-product partnership. - Comfort working in a regulated industry, reasoning about bias, explainability, and patient safety in ML systems. - Practiced perspective on AI-augmented engineering workflows.