Field AI Product Lead — Construction Tech

Coinscapture; Northern (KY); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Homebound is seeking a Staff Product Manager for Physical AI to own the technology that brings AI to construction sites. You will drive the field AI strategy, translate vision into on-site products, and work across Construction Operations, Field Operations, and Engineering to deploy and improve AI features in real-world settings.

You don’t need a construction background, but you must ship AI that performs in dusty, variable job sites, with on-device inference and rigorous safety reviews.

Qualifications

• 8+ years of product management, including end-to-end ownership of a complex product.
• Track record of shipping applied AI (computer vision, multimodal, LLM/agent-based) in production.
• Technical fluency to work with engineers and ML teams on system tradeoffs.
• Knowledge of emerging AI frontiers and readiness for deployment decisions.
• Experience moving a product concept to launch in ambiguous environments.
• Clear communication bridging field personnel, ML engineers, and executives.

Responsibilities

• Own the field AI strategy and roadmap end-to-end across the field portfolio.
• Ship AI into the physical environment on active job sites.
• Define evaluation and quality frameworks for AI features and safety reviews.
• Drive cross-team collaboration across Construction Ops, Field Ops, and Eng.
• Write actionable requirements and measure success with experiments.

Skills

Product management

Applied AI shipping

Technical fluency

AI frontier knowledge

End-to-end ownership

Job description

Homebound is seeking a Staff Product Manager for Physical AI to own the technology that brings AI to construction sites. You will drive the field AI strategy, translate vision into on-site products, and work across Construction Operations, Field Operations, and Engineering to deploy and improve AI features in real-world settings.

You don’t need a construction background, but you must ship AI that performs in dusty, variable job sites, with on-device inference and rigorous safety reviews.