Founding Engineer
Role summary
Relari is seeking a Founding Engineer to help build the foundation for creating, verifying, and deploying intelligent agents for AI-native software. The role involves architecting core systems to translate natural language specifications into structured agent behavior, building infrastructure for tool/API integration, and ensuring agent reliability through simulation and automated evaluation. The engineer will also work directly with customers and explore new agent reasoning capabilities. Ideal candidates have experience building complex systems, are comfortable in fast-paced, unstructured environments, and operate with an owner's mindset. Bonus points for experience with AI agent frameworks, foundation models, or open-source contributions.
In this role, you will:
* **Architect core systems** for Nuvi — designing how natural language specifications are turned into structured, testable agent behavior.
* **Build infrastructure** that enables agents to integrate with real-world tools, APIs, and knowledge sources.
* **Ensure agent reliability** by developing systems for simulation, automated evaluation, and behavioral verification.
* **Work directly with customers**, helping them go from intent to working agents — and using that feedback to inform what we build next.
* **Continuously explore new ways agents can reason, act, and improve** — and bring those innovations into the product.
You’re a good fit if:
* You’ve built and shipped technically complex systems — whether infrastructure, AI tools, or end-user products.
* You’re a Software Engineer, Machine Learning Engineer, or Research Engineer at a fast-growing software company or top research lab.
* You thrive in a fast-moving environment and view unstructured environments as opportunities to identify the most impactful work and define the future success of the company.
* You operate like a owner: you identify high-leverage problems, drive them to resolution, and raise the bar for the team.
Bonus points if you have:
* Built or contributed to an AI agent framework, orchestration layer, or evaluation pipeline.
* Trained or fine-tuned foundation models, or worked on tools that enable others to do so.
* Published in top ML/NLP conferences (e.g. NeurIPS, ICML, ACL) or contributed to widely used open-source projects.
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