Lead Product Manager, AI Agents

SuperDial

1d•$200,000 - $250,000•Hybrid

About The Position

SuperDial is building AI agents that do real work inside healthcare revenue cycle operations. Our agents help customers automate high-volume, manual workflows such as claims status follow-up, eligibility, benefits verification, prior authorization, and payer communications — work that is critical to healthcare organizations, but still far too manual, fragmented, and time-intensive today. This is a rare opportunity to build applied AI in a domain where reliability, quality, and measurable customer impact matter deeply. As a Lead Product Manager, AI Agents at SuperDial, you will report to the VP of Product and own a core AI agent product area. You will work closely with Engineering, Operations, and customers to understand complex healthcare workflows, define what great agent performance looks like in production, and ship improvements that make our agents more reliable, useful, and scalable. This role is ideal for a product leader who is technically fluent, customer-centered, analytical, and energized by ambiguity. You will join early in the development of our Product function and help shape not only what we build, but how we build.

Requirements

6+ years of relevant experience, including 4+ years in product management or a product-adjacent ownership role within a B2B software environment.
Experience working on AI, automation, data-heavy, workflow, healthcare, revenue cycle, or technically complex software products where quality depends on system behavior, data inputs, edge cases, and operational performance.
Strong interest in applied AI and comfort partnering with Engineering to improve agent behavior, workflow logic, automation quality, and production performance.
Ability to quickly understand healthcare revenue cycle workflows, including claims, eligibility, benefits verification, prior authorization, payer/provider operations, billing workflows, or similar operational domains.
Strong technical fluency, including the ability to work with Engineering, Data, and Operations on product behavior, integrations, APIs, data ingestion, data quality, and workflow logic.
Strong analytical judgment, including the ability to define success metrics, diagnose root causes, and use data to guide product decisions.
Customer-facing experience, with the ability to ask precise questions, synthesize complex needs, and communicate clearly with technical, operational, and executive stakeholders.
Strong product execution instincts, including the ability to scope work, manage tradeoffs, clarify requirements, support QA, and drive delivery in a fast-moving environment.
Comfort operating in an early-stage, high-growth environment where priorities evolve quickly and process is still being built.

Nice To Haves

Direct experience in healthcare revenue cycle management, payer/provider workflows, claims status, eligibility, prior authorization, denials, billing operations, or RCM services.
Direct experience building or deploying AI agents, LLM-powered products, voice AI, automation platforms, or AI-enabled workflow software.
Familiarity with modern AI infrastructure and tooling, such as LLM APIs, agent frameworks, evaluation tools, vector databases, retrieval systems, or AI-assisted development tools.
Experience working in a GCP-based environment, including tools such as BigQuery, Vertex AI, Cloud Functions, Cloud Run, Pub/Sub, or related data and AI infrastructure.
Experience with products involving integrations, APIs, data ingestion, customer-specific workflow configuration, or complex implementation paths.
Ability to read SQL, inspect logs, analyze product behavior, or prototype with AI-assisted tools.

Responsibilities

Own a core AI agent product area focused on improving agent reliability, workflow completion, customer value, and scalability.
Define product strategy, priorities, success metrics, and roadmap tradeoffs in partnership with Product, Engineering, Operations, and customer-facing teams.
Develop a deep understanding of healthcare revenue cycle workflows and translate customer needs into clear product requirements.
Use product data, operational insights, customer feedback, and production behavior to diagnose issues, identify opportunities, and guide prioritization.
Partner closely with Engineering to scope, build, QA, launch, and iterate on product improvements.
Test and validate new AI agent capabilities with customers and internal teams using pilots, production data, and feedback loops.
Define how SuperDial measures agent quality, workflow completion, reliability, escalation, customer value, and operational efficiency.
Support go-to-market and customer-facing teams with clear product guidance, including supported use cases, product direction, and how capabilities create customer value.
Build repeatable product patterns, playbooks, and operating systems that help SuperDial scale deployments across customers.