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Founding Engineer (Applied AI)

About the Role This is a founding engineering role at an early-stage construction technology startup using AI to automate blueprint analysis, material takeoffs, and cost estimation. You'll own the intelligence layer, advancing com...

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Clera (ashby) San Francisco, California, United States Source published Sep 15, 2026 Verified 4 hours ago
✓ 80% verification score · Source: Clera (ashby) · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
EmploymentFull Time
CountryUnited States
DepartmentEngineering

Overview

About the Role This is a founding engineering role at an early-stage construction technology startup using AI to automate blueprint analysis, material takeoffs, and cost estimation. You'll own the intelligence layer, advancing computer vision and AI systems from research to shipped prototypes that real users rely on. It's a high-impact position on a small, focused team working to make housing development faster and more affordable. What You'll Do Drive the intelligence layer of the product, not the application layer. Apply the latest computer vision models, LLMs, and AI systems to hard construction-domain problems. Ship working prototypes quickly and iterate based on direct user feedback. Coordinate and orchestrate multiple ML models as part of a broader AI system. Take on pragmatic early-stage work outside the core modeling problem when the team needs it. What We're Looking For Hands-on

Full job description

Full Job Description

About the Role

This is a founding engineering role at an early-stage construction technology startup using AI to automate blueprint analysis, material takeoffs, and cost estimation. You'll own the intelligence layer, advancing computer vision and AI systems from research to shipped prototypes that real users rely on. It's a high-impact position on a small, focused team working to make housing development faster and more affordable.

What You'll Do

  • Drive the intelligence layer of the product, not the application layer.

  • Apply the latest computer vision models, LLMs, and AI systems to hard construction-domain problems.

  • Ship working prototypes quickly and iterate based on direct user feedback.

  • Coordinate and orchestrate multiple ML models as part of a broader AI system.

  • Take on pragmatic early-stage work outside the core modeling problem when the team needs it.

What We're Looking For

  • Hands-on applied computer vision and machine learning experience, including Python and deep learning.

  • Demonstrated ability to take an AI system from zero to one and deploy it in production.

  • Strong CS or engineering fundamentals, whether from formal education or equivalent practical experience.

  • Background at a fast-moving tech company, research lab, or early-stage startup is a plus.

  • Empathetic, candid communicator with a growth mindset, slope matters more than starting point.

  • Comfortable doing "schlep" work as part of building early-stage products.

  • Bonus: previously built something that users adopted or paid for, or has a construction industry background.

  • US citizenship required; visa sponsorship is not available.

Compensation & Benefits

Base salary: $120,000 to $180,000 USD annually. Equity is expected given the founding-team nature of the role. Visa sponsorship is not available.

Location

On-site, full-time in San Francisco, California. Relocation is welcome.

Tips for this job

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  1. Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
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  3. Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
  4. Apply through the original employer or official recruitment destination shown on this page.

Verification notes

Discovered directly from the employer’s public Ashby Job Postings API. The complete public role content and compensation metadata were normalized into safe candidate-facing sections.

Original authoritative source

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