Jobs · Engineering · New York

Founding Engineer (+ Equity) at Truvo

Jack & Jill · New York, NY · Yesterday
On-siteEngineeringFull-time

Truvo is a seed-stage NYC insurtech startup backed by the founders of MongoDB and Indeed, building a fully autonomous AI-native insurance broker.

About the role

Join Truvo as a Founding Engineer to build AI agents and models that automate a trillion-dollar industry. You will own the end-to-end development of production-grade LLM systems, creating deep API integrations with legacy carrier rails to save households hundreds of dollars. This is a high-impact, IC role for scrappy, first-principles builders.

Location: New York City, USA

Why this role is remarkable

  • True founding-level ownership with 0.4-1% equity and the chance to shape the product, technical architecture, and culture from the ground up.
  • Backed by an elite group of investors including the founders of Wayve, Indeed, and MongoDB, alongside early team members from Google and Meta.
  • Tackle the massive complexity of rewiring the antiquated trillion-dollar insurance industry using cutting-edge AI agents and multi-agent systems rather than just prototypes.

Responsibilities

  • Design and deploy production-grade AI agents and multi-agent systems using frameworks like LangChain or LangGraph to automate complex insurance workflows.
  • Engineer deep API integrations into legacy insurance carrier systems to enable autonomous quoting, binding, and policy servicing at scale.
  • Collaborate directly with the CEO and technical leadership to move from zero to one, shipping code to production from day one.

Requirements

  • Proven experience building and fine-tuning production LLM systems and AI agents, with strong foundations in computer science and scalable system architecture.
  • Exceptional startup grit and high agency demonstrated through previous technical co-founder roles or being an early-stage employee at a high-growth startup.
  • Scrappy, hands-on builder who thrives in ambiguity and is comfortable working across the stack to solve messy, high-impact problems.

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