Founding AI Engineer

This is a founding engineering role at a seed-stage construction AI company building the intelligence layer that transforms messy blueprints, permits, and construction documents into actionable insights - making building more efficient and equitable. The company applies state-of-the-art computer vision, LLMs, and multimodal AI to automate and improve cost estimation and permit workflows, with direct impact on housing, hospitals, and schools.

As one of the earliest engineers, you’ll have outsized influence over the technical direction, system architecture, and culture of the team. This is a full-time, in-office role in San Francisco.

• Hands-on, applied experience with computer vision, multimodal AI, and/or LLMs - trained and deployed models in production on real-world documents or similarly messy data.
• 0-to-1 builder mindset: you’ve launched an AI system into production or built a core part of a new product.
• Comfortable owning the full ML lifecycle - from data and experimentation through deployment and iteration.
• Strong communication skills; able to explain results and tradeoffs clearly to teammates and stakeholders.
• Growth mindset, intellectual honesty, and an empathetic collaborative style.
• Willingness to roll up your sleeves on practical engineering work when the team needs it.
• US work authorization required (visa sponsorship is not available).
• Based in San Francisco or willing to relocate - this is a full-time in-office role., * Experience building something people used or paid for (personal projects, startups, or prior founding experience counts).
• Familiarity with the construction domain or similar technical document-heavy industries.
• Any experience level considered - new grads with strong applied ML portfolios are welcome.

• Apply state-of-the-art computer vision, LLM, and multimodal AI techniques to real-world construction documents (noisy, heterogeneous inputs).
• Own and prioritize the intelligence layer - modeling, data pipelines, and inference - over application UI.
• Rapidly prototype models and iterate based on direct user feedback and real production data.
• Build and deploy end-to-end production AI systems at scale.
• Collaborate closely with product and users to surface and solve high-impact problems.
• Embrace pragmatic engineering work including data labeling, ETL, and deployment tasks as part of a small team.
• Communicate progress, tradeoffs, and decisions with clarity and empathy across the team.