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Hyperexponential is building an agentic platform for commercial insurance underwriting. This role sits in Field Engineering, the technical core of the go-to-market organization, at the intersection of product, sales, and customer success.
As an Agent Builder, you will be a hybrid engineer-solutions lead responsible for scaling proven customer acquisition motions across the US market. You'll work directly with insurers from initial custom demos through live deployment and expansion, building AI agents that solve real underwriting problems.
Key responsibilities include:
- Running hands-on five-day bootcamps at customer sites, building working AI agents on their real problems
- Taking builds live so customers use them to write real business
- Setting clear goals with each customer upfront and demonstrating measurable results that drive expansion decisions
- Training customers' own teams to build independently on the platform
- Converting custom builds into reusable components for teammates
- Sharing field learnings with the product team to inform platform development
- Building and running custom demos for prospective customers alongside sales leads
- Leading technical conversations with senior underwriters and actuaries
You'll be comfortable operating at the edge of what's possible for the product, combining deep technical understanding with domain insight and creativity. The culture emphasizes high ownership, collaboration, and visibility—every engagement is high-stakes and feeds directly into revenue outcomes and product direction. You'll partner closely with Sales and Product Engineering, acting as a trusted advisor throughout the sales cycle and beyond.
Hyperexponential is a global team of nearly 300 people across London, New York, and Warsaw. The company is backed by a16z, Highland Europe, and Battery Ventures, and leading global carriers and MGAs use hx to support underwriting across more than $75bn of annual commercial P&C premium.
Requirements:
- 3+ years writing production code (Python) for large or regulated businesses
- Recent, hands-on experience building with AI agents, with demonstrated discipline in validating agent output before customer reliance
- Confidence presenting to expert, skeptical customers, with examples of customer feedback influencing your work
- Proven track record of owning outcomes, not just following specifications
- Drive to push initiatives through without being asked
- Customer-facing engineering experience at an AI company
- Public examples of things you've built
- Insurance, actuarial, or pricing experience (not required—the company will provide training)
