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Tulip Interfaces is seeking an Engineering Leader to own the architecture and delivery of its agent-native app-building platform. You will hire and coach 3–5 engineers and drive standards for testing, observability, and AI-driven development.
You will partner with Product, Design, Infrastructure, and customers to ensure high-quality releases and scalable systems in a multi-cloud environment.
You bring high levels of technical acumen and people management skills, years of building and delivering SaaS products, and you’re energized by the chance to build at the edge of what AI models can do
You have strong opinions about agent architectures, context management, and building scalable systems, and you’re passionate about mentoring engineers and helping them grow
You’re a collaborative, motivated self-starter who leads from the front, brings your authentic self to work, and wants to help shape how this team operates as a reference point for AI-driven development across the broader engineering org
You have strong product-engineering instincts — you can prototype quickly, prune scope, and decide what to ship versus what to throw away
Expertise in contemporary tech stacks including React, Node.js, MongoDB, and Postgres, with experience deploying in multi-cloud and multi-tenant environments
3+ years of people management experience leading a software development team, with hands-on technical leadership, architecting solutions, and staying close to the code
Bachelor’s degree in Computer Science or related field, or equivalent working experience
Experience building and scaling AI systems in production, including, agent orchestration and context management, retrieval pipelines and vector stores, tool invocation and MCPs
Active use of AI tooling in your own practice, with a point of view on how it changes the way teams build
7+ years experience in software engineering, with experience building and scaling large SaaS products and complex distributed systems
Experience designing and running evaluations for agentic systems — defining success criteria, building task suites, and measuring reliability across multi-step tool use