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Salary: USD 180,000 - 235,000 / annual
Liberate builds insurance-native AI agents that handle customer interactions end-to-end across voice, SMS, email, and digital channels, then execute downstream work inside core systems like Guidewire, Duck Creek, Applied Epic, and Salesforce. The company positions itself as the "System of Action for Insurance"—agents don't just talk, they execute and resolve. The moat is production depth: real regulated conversations, real workflows, and the governance and trust layer that carriers and agencies require.
As a Staff Agent Product Manager, you will own ambiguous, high-stakes problems for enterprise insurance customers end-to-end. You will frame what to build, design the agent systems that deliver it, navigate hard product and engineering tradeoffs, and own the outcome through to measurable, in-production value. This is fast-paced, hands-on, customer-facing work for someone who wants to build and operate, not just recommend.
Key responsibilities include: structuring messy, undefined problems with incomplete information and driving them to measurable customer outcomes; leading executive-level customer engagements and earning the trust of customer leadership; applying structured, analytical problem-solving to segment problems, quantify impact, and prioritize ruthlessly; partnering with engineering on high-leverage tradeoffs to separate launch-critical needs from nice-to-haves and distinguish customer-specific config from reusable platform capability; owning delivery, adoption, and measurable value in production; and creating reusable patterns so recurring solutions become scalable product, platform, playbook, or process improvements.
Required: 8+ years of professional experience including 5+ years in management consulting; strong academic record and clear analytical spike; proven enterprise customer-facing ownership (outcomes, not just advice); demonstrated experience owning a messy implementation or customer outcome from start to finish; exceptional structured thinking and communication; high agency and comfort operating in ambiguity with competing constraints; clear upward trajectory and evidence of raising the bar.
Nice to have: experience with AI/ML systems, workflow automation, or agent-based products; background in insurance, fintech, or regulated industries; familiarity with system design tradeoffs and technical architecture; track record of productizing or creating reusable patterns from implementations.
