About the role
At Trunk Tools, we’re the leading AI company revolutionizing construction, the second-largest industry on earth. We recently raised a $40M Series B led by Insight Partners, bringing our total funding to $70M from top-tier investors including Redpoint and Innovation Endeavors. This new round is fueling our next phase of growth as we scale AI agents across the jobsite. Our mission is to build the future of construction through intelligent automation. Despite being a $13+ trillion industry, construction still runs largely on analog processes, we’re changing that by embedding AI directly into field operations. Founded by builders and technologists (Stanford, MIT), our team has delivered software used by over 140,000 field professionals, impacting millions of users and contributing to $10B+ in built projects. Many of us come from the field ourselves, giving us a deep understanding of the industry’s unique challenges. After years of building the “brain” of construction, we’re now launching production-ready AI agents, starting with intelligent document processing and Q&A, and rapidly expanding into core operational workflows. Our team has doubled in the past year, and with 100+ employees (50+ engineers), we’re scaling fast and entering a period of hypergrowth. This is a rare opportunity to join at an inflection point. What you’ll do Own and evolve core parts of our AI platform, spanning search, retrieval, and multi-agent orchestration. Lead the design and build-out of a multi-agent system , including defining new agent “pillars” and how they collaborate in production. Architect and scale document processing pipelines (Kubernetes-based), including ingestion, parsing, chunking, indexing, and structured data extraction across heterogeneous document types. Design and iterate on search and retrieval systems (ranking, query understanding, QA) grounded in real user behavior and product needs. Help build and expand our knowledge graph , modeling entities and relationships to power downstream reasoning and retrieval. Drive ambiguous, high-impact projects end-to-end, from problem definition through experimentation and productionization. Establish and improve evaluation and experimentation frameworks to measure system quality and guide iteration. Write clean, maintainable, production-grade code and raise the bar for engineering quality across
