Founding AI​/ML Engineer

About the Role

Our company is hiring a Founding AI / ML Engineer to help architect and ship the next generation of AI-powered workflows for construction. This is not a research seat or a demo-builder role. The company wants product- minded engineers who build reliable, production-grade AI systems that solve real customer problems in lean teams. You will work across multimodal AI, document intelligence, agentic workflows, and evaluation systems, turning cutting-edge capabilities into software estimators that depend on daily.

You will report to the Chief AI Officer (eventually CTO) and work directly alongside the founders and customers.

This is initially a full IC role: ship code, deploy features, test with users, close the loop. Management track is available within the near term for those who want it, but is not the initial scope.

• Build and ship production-grade AI systems used by real customers
• Design multimodal pipelines for processing drawings, specifications, and construction documents
• Build agentic workflows that automate pre-construction tasks: document intake, extraction, takeoff support, bid preparation, QA
• Build and improve retrieval systems, evaluation systems, and orchestration layers
• Improve model quality, latency, reliability, and cost efficiency Prototype and iterate on customer-facing AI features
• Own systems end-to-end from experimentation through deployment
• Help shape engineering culture and technical direction

• 6+ years of engineering experience , with at least one prior role at an early-stage high-growth startup
• Production-grade AI systems shipped to real customers: practical hands-on experience with LLMs, computer
• vision, segmentation models, or other applied ML systems in production
• Retrieval systems experience: embeddings, reranking, chunking, source-grounded answers, knowledge bases
• Agentic workflow experience: tools, APIs, state management, retries, guardrails, observability
• Strong backend fundamentals: APIs, async jobs, queues, databases, cloud deployment, and monitoring. End-
• to-end ownership from problem definition through deployment.

• Construction / AEC / glazing / curtain wall / architectural metals personal or professional exposure
• Multimodal AI, computer vision, or document intelligence / OCR background
• Prior experience converting human-operated workflows into a product (AI-native services thesis adjacency)
• Inference optimization or evaluation systems background

NB: VISA SPONSORSHIP - H-1B