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.

What You'll Own

1. Build and ship production-grade AI systems used by real customers
2. Design multimodal pipelines for processing drawings, specifications, and construction documents
3. Build agentic workflows that automate pre-construction tasks: document intake, extraction, takeoff support, bid preparation, QA

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4. Build and improve retrieval systems, evaluation systems, and orchestration layers
5. Improve model quality, latency, reliability, and cost efficiency Prototype and iterate on customer-facing AI features
6. Own systems end-to-end from experimentation through deployment 
7. Help shape engineering culture and technical direction

Requirements Must-Have

1. 6+ years of engineering experience , with at least one prior role at an early-stage high-growth startup 

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2. Production-grade AI systems shipped to real customers: practical hands-on experience with LLMs, computer
3. vision, segmentation models, or other applied ML systems in production 
4. Retrieval systems experience: embeddings, reranking, chunking, source-grounded answers, knowledge bases 
5. Agentic workflow experience: tools, APIs, state management, retries, guardrails, observability 
6. Strong backend fundamentals: APIs, async jobs, queues, databases, cloud deployment, and monitoring. End-
7. to-end ownership from problem definition through deployment. 

Nice-to-Have

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

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