Engineering Manager, Case Strategy

About Pivotal Health

Pivotal Health is the leading technology platform that helps healthcare providers get paid fairly in an increasingly complex reimbursement landscape.

Today, many providers face persistent underpayment from health insurance companies, despite delivering high-quality care. While processes like IDR (Independent Dispute Resolution) were designed to promote fairness, they’re often administrative-heavy, time-consuming, and difficult to navigate without the right tools.

Pivotal Health combines software, data, and service into a seamlessly integrated, AI-driven platform that simplifies these complex reimbursement workflows. We help providers efficiently dispute underpaid claims, reduce administrative burden, and recover the reimbursement they’re entitled to; without adding more work to already stretched teams.

Our full-service IDR solution is just the starting point. We’re building solutions that enable providers to operate with clarity, control, and confidence across the reimbursement journey.

About the Role

We’re looking for an Engineering Manager to lead our Case Strategy Engineering team as we expand one of the most consequential areas of Pivotal’s product. You’ll own the engineering systems that turn data, AI, and machine-learning capabilities into better strategies for offers, IDRE selection, position statements, batching, and other critical decisions across the IDR workflow.

You’ll lead an experienced team of engineers and work closely with our Data Science and AI Platform teams to bring new models into production. The team owns the full engineering layer around those capabilities—including data models, databases, APIs, feedback systems, and full-stack product experiences.

This role reports to the Director of Product Engineering. It is a high-accountability role: the problems are ambiguous and the outcomes directly influence customer lift and annual recurring revenue. The right person will find that combination energizing.

What You'll Do

Own Customer Lift: Drive the engineering outcomes that improve the incremental reimbursement Pivotal delivers for customers. Connect the team’s technical roadmap to measurable gains in customer value and ARR.

Build the Case Strategy Platform: Own the data models, databases, APIs, services, and full-stack workflows that apply case strategies throughout the IDR process.

Turn Models Into Product Capabilities: Work with Science and AI Platform to move AI and ML capabilities from experimentation into reliable, usable product experiences.

Create a Data and Feedback Flywheel: Build the systems that capture outcomes, surface useful signals, and feed real-world results back into model development and strategy improvement.

Lead an Experienced Engineering Team: Set clear priorities, coach engineers, create accountability, and make sound decisions while preserving the rigorous technical debate that makes the team stronger.

Grow and Structure the Organization: Hire additional engineers and establish the roles, ownership boundaries, and operating practices the team needs as its scope expands.

Drive Predictable Delivery: Turn ambiguous product and technical problems into clear plans, sequence the work, manage dependencies, and ensure the team ships meaningful improvements consistently.

Shape the Technical Direction: Guide architectural decisions across Python services, GCP infrastructure, data systems, LLM-enabled capabilities, and customer-facing applications.

Use AI to Stay Close to the Work: Apply AI-enabled development tools to explore ideas, prototype solutions, and contribute directly when doing so accelerates the team or improves a technical decision.

What We're Looking For:

You have spent several years (7+ as an IC and 2+ as a manager) building and leading engineering teams in early-stage environments where the structure, roadmap, and technical approach were still being defined.

You bring a strong individual-contributor foundation and enough technical depth to guide architecture, evaluate tradeoffs, and earn the trust of experienced engineers.

You have led the delivery of production AI or ML products, or managed engineering work in close partnership with data-science or applied-science teams.

You understand how to build reliable systems around models, including data pipelines, feedback loops, APIs, monitoring, and the product workflows where model outputs are applied.