The
Director, Applied AI & ML Engineering will lead the strategy, architecture, and deployment of intelligent systems that transform how homes are designed, estimated, and built. This role will drive the integration of AI and machine learning across the residential construction lifecycle-from digital plan understanding and takeoffs to automated estimating, material optimization, and design personalization.
The ideal leader blends technical depth with strategic clarity-able to guide teams across computer vision, large language models, and agentic automation while ensuring reliable, scalable delivery within the construction domain.
What You Will Do:- Define and lead the Applied AI & ML strategy for residential construction, identifying and prioritizing use cases that enhance speed, accuracy, and efficiency.
- Build and maintain a roadmap of agentic AI systems that automate key construction workflows-such as plan interpretation, quantity takeoffs, cost estimation, and material specification optimization, while enabling seamless integration with suppliers, ERP platforms, and technology providers.
- Partner with Product, Engineering, and Operations leaders to embed AI capabilities into core platforms and customer-facing applications.
- Lead the design of AI-powered and multi-agent systems that connect workflows across design, estimating, procurement, and field execution.
- Architect retrieval-augmented generation (RAG) and computer vision pipelines that interpret plan sets, generate takeoffs, and surface contextual insights.
- Combine LLMs, CV, and rule-based logic to deliver explainable and auditable systems tailored to construction professionals.
- Ensure architectural scalability, performance, and observability in all deployed systems.
- Oversee the end-to-end ML lifecycle-from experimentation and model development to deployment, monitoring, and iteration.
- Implement best practices in MLOps, data management, and continuous delivery pipelines.
- Deliver measurable improvements in model quality, reasoning accuracy, and cost efficiency through advanced evaluation methods, such as Evals, zero- and few-shot benchmarking, Chain-of-Thought, and LLM-as-a-judge techniques to guide continuous model refinement.
- Build, mentor, and lead a cross-functional team of applied AI and ML engineers, partnering closely with product, design, and software engineering teams to deliver production-grade AI-powered systems.
- Foster a culture of collaboration, experimentation, and responsible AI development.
- Manage vendor relationships and technology partnerships across cloud and AI platforms.
- Collaborate with design, estimating, and operations teams to identify automation opportunities and ensure successful adoption.
- Translate complex AI concepts into clear direction for business and product stakeholders.
- Represent the organization's AI vision in external partnerships, technical forums, and industry collaborations.
What You Need to Succeed:- 12+ years of experience in applied AI, ML, and/or Software engineering, with at least 5 years in a leadership role.
- Bachelor's or advanced degree in Computer Science, Machine Learning, or a related field preferred.
- Proven success designing and deploying AI-driven systems in production environments.
- Expertise in LLMs, computer vision, multimodal models, and retrieval-augmented generation (RAG) architectures.
- Strong foundation in modern software engineering-including APIs, microservices, CI/CD, and containerization.
- Hands-on familiarity with ML platforms such as MLflow, Kubeflow, or SageMaker for model training and deployment.
- Demonstrated ability to collaborate across engineering, product, and operations in a complex technical environment.
- Excellent written and verbal communication skills for both technical and executive audiences.
- Experience in residential construction technology, including estimating, takeoffs, or design automation is preferred.
- Background in BIM/CAD integration, digital twin platforms, or 3D modeling workflows is preferred.
- Familiarity with agent orchestration frameworks (Temporal, n8n, LangGraph) and enterprise API integration is preferred.
- Understanding of AI governance, auditability, and human-in-the-loop validation frameworks is preferred.
- Experience with Azure, AWS, or GCP cloud platforms for scalable AI deployment is preferred.
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