About the RoleThis is a founding engineering role at an early-stage construction-tech AI startup, where you'll build the core intelligence layer that transforms messy construction documents - blueprints, permits, and more - into actionable cost and workflow insights. Your work will directly shape systems that make housing, hospitals, and schools faster and cheaper to build.
What You'll Do- Apply state-of-the-art computer vision, LLM, and multimodal AI techniques to real-world construction documents.
- Own the intelligence layer - modeling, data pipelines, and inference - rather than application UI.
- Rapidly prototype models and iterate based on user feedback and live data.
- Build and deploy production AI systems that handle noisy, heterogeneous inputs.
- Collaborate closely with product and end users to surface and solve high-impact problems.
- Take on pragmatic engineering work including data labeling, ETL, and deployment as needed on a small team.
What We're Looking For- Hands-on applied computer vision and machine learning experience, including training and deploying models to production.
- Proven 0-to-1 builder mindset - experience launching AI systems or building core parts of a new product in an early-stage environment.
- Experience working with multimodal data (images, text, documents) in production settings.
- Familiarity with modern CV, LLM, and multimodal model approaches and tooling.
- Ability to iterate quickly on prototypes based on real user feedback.
- Strong communication skills and an empathetic, collaborative approach to working across a small team.
- Willingness to handle broad engineering tasks - no task is too small.
- US work authorization required; visa sponsorship is not available.
- All experience levels considered, including new graduates.
- Construction domain knowledge is a plus but not required.
Compensation & BenefitsSalary range:
$120,000 - $180,000 USD annually. Equity is expected given the founding-team nature of the role. Visa sponsorship is not available.
LocationOn-site, full-time in
San Francisco, CA, USA. Candidates must be based in San Francisco or willing to relocate.
