Founding AI Engineer position at a seed-stage startup building the intelligence layer for the construction industry. You will work hands-on with state-of-the-art computer vision, large language models, and multimodal AI to prototype and ship production systems that operate on real-world, noisy construction documents. The focus is on the intelligence layer—modeling, data pipelines, and inference—rather than the application UI. This is a full-time, in-office role based in San Francisco, CA. US work authorization is required; visa sponsorship is not available.

What You’ll Do - Apply state-of-the-art computer vision, LLM, and multimodal AI techniques to real-world construction documents (blueprints, schedules, specs). - Rapidly prototype models and iterate based on user feedback and real production data. - Build and deploy end-to-end AI systems that operate reliably on noisy, heterogeneous inputs. - Own the intelligence layer: modeling, data pipelines, and inference infrastructure. - Collaborate closely with product and users to surface and prioritize high-impact problems. - Embrace practical engineering work—data labeling, ETL, deployment—as part of a small, high-trust team. - Communicate progress, tradeoffs, and results clearly and empathetically across the team.

Founding AI Engineer position at a seed-stage startup building the intelligence layer for the construction industry. You will work hands-on with state-of-the-art computer vision, large language models, and multimodal AI to prototype and ship production systems that operate on real-world, noisy construction documents. The focus is on the intelligence layer—modeling, data pipelines, and inference—rather than the application UI. This is a full-time, in-office role based in San Francisco, CA. US work authorization is required; visa sponsorship is not available.

What You’ll Do - Apply state-of-the-art computer vision, LLM, and multimodal AI techniques to real-world construction documents (blueprints, schedules, specs). - Rapidly prototype models and iterate based on user feedback and real production data. - Build and deploy end-to-end AI systems that operate reliably on noisy, heterogeneous inputs. - Own the intelligence layer: modeling, data pipelines, and inference infrastructure. - Collaborate closely with product and users to surface and prioritize high-impact problems. - Embrace practical engineering work—data labeling, ETL, deployment—as part of a small, high-trust team. - Communicate progress, tradeoffs, and results clearly and empathetically across the team.