Overview
ABOUT THE ROLE
Full job description
ABOUT THE ROLE This is a founding engineering role on a small, high-impact team building AI systems that automate blueprint analysis and cost estimation for the construction industry. You will own the intelligence layer, not the application layer, driving computer vision and machine learning research all the way to production-ready prototypes that real users rely on. The work directly supports a mission to make housing and infrastructure faster and more affordable. WHAT YOU'LL DO
- Lead development of the intelligence layer, including computer vision pipelines and coordinated ML model systems, rather than application-layer features.
- Apply the latest computer vision models, large language models, and AI systems to hard, real-world construction problems.
- Ship initial prototypes quickly, then iterate and improve them based on direct user feedback.
- Take on pragmatic early-stage work outside the core modeling problem as needed to move the product forward. WHAT WE'RE LOOKING FOR
- Hands-on applied computer vision and machine learning experience, including Python and deep learning frameworks.
- Demonstrated ability to take an AI system from zero to one and deploy it in production.
- Background from a strong engineering or research environment, such as an autonomous systems, robotics, or applied AI team.
- Growth mindset: you prioritize trajectory and learning over prior credentials.
- Empathetic, candid communicator who is honest about what is working and what is not.
- Comfortable doing the full range of early-stage work that comes with building a product from scratch.
- Genuine motivation to make housing more affordable, not just to maximize compensation.
- Nice to have: construction or social-impact background, or prior experience building something users adopted or paid for. COMPENSATION & BENEFITS Base salary: $120,000 to $180,000 USD annually. Equity participation is expected at the founding engineer level. Visa sponsorship is not available; US work authorization is required. LOCATION Full-time, on-site in San Francisco, California. Candidates must be based in or willing to relocate to San Francisco.
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