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Pivotal Health is a technology platform helping healthcare providers navigate complex reimbursement landscapes and dispute underpaid claims through an AI-driven, full-service solution. The company combines software, data, and service to simplify IDR (Independent Dispute Resolution) workflows and help providers recover entitled reimbursement without adding administrative burden.
You will lead the Case Strategy Engineering team, owning the engineering systems that translate data, AI, and machine-learning capabilities into better strategies for offers, IDRE selection, position statements, batching, and other critical decisions across the IDR workflow. This is a high-accountability role reporting to the Director of Product Engineering, where technical outcomes directly influence customer value and annual recurring revenue.
Key responsibilities include:
- Driving engineering outcomes that improve incremental reimbursement delivered to customers and connecting the technical roadmap to measurable ARR gains
- Building the Case Strategy Platform: owning data models, databases, APIs, services, and full-stack workflows that apply case strategies throughout IDR
- Translating AI and ML capabilities from experimentation into reliable, production-ready product experiences in partnership with Data Science and AI Platform teams
- Creating a data and feedback flywheel that captures outcomes, surfaces signals, and feeds real-world results back into model development
- Leading an experienced engineering team: setting priorities, coaching engineers, creating accountability, and fostering rigorous technical debate
- Growing and structuring the organization by hiring engineers and establishing roles, ownership boundaries, and operating practices
- Turning ambiguous product and technical problems into clear plans, sequencing work, managing dependencies, and ensuring consistent delivery
- Guiding architectural decisions across Python services, GCP infrastructure, data systems, LLM-enabled capabilities, and customer-facing applications
- Using AI-enabled development tools to explore ideas, prototype solutions, and contribute directly when it accelerates the team or improves technical decisions
Requirements:
- 7+ years as an individual contributor and 2+ years as a manager building and leading engineering teams in early-stage environments where structure, roadmap, and technical approach were still being defined
- Strong individual-contributor foundation with sufficient technical depth to guide architecture, evaluate tradeoffs, and earn trust of experienced engineers
- Experience leading delivery of production AI or ML products, or managing engineering work in close partnership with data-science or applied-science teams
- Understanding of how to build reliable systems around models, including data pipelines, feedback loops, APIs, monitoring, and product workflows where model outputs are applied
- Ability to lead through ambiguity and imperfect data, forming clear points of view without waiting for every question to be resolved
- Skill in managing experienced, opinionated teams—creating clarity and making decisions without shutting down productive disagreement
- Ability to connect engineering work to business and customer outcomes, with willingness to own results tied directly to ARR
- Strong communication of technical decisions with precision and ability to create alignment across Engineering, Science, Product, and other partners
Extra credit experience:
- Healthcare, provider reimbursement, revenue-cycle management, claims, or Independent Dispute Resolution background
- Experience with optimization, experimentation, decision systems, strategy models, or feedback-driven ML products
- Experience scaling early engineering teams or building production AI systems using Python, GCP, and LLMs
