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# Founding AI Engineer

- **Company:** CLERA, LLC
- **Location:** San Francisco, CA, United States
- **Salary:** $120,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Extract Transform Load (ETL), Operational Databases, Large Language Models, Build Management, Data Pipelines
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=264de2d9290626b8

## About the Role

Must-haves:

 * Demonstrated experience applying computer vision and/or multimodal AI in production - on real-world, messy data (documents, images, mixed modalities).
 * 0-to-1 builder mindset: you've launched an AI system into production or built the core of a new product from scratch.
 * Hands-on experience with LLMs and modern CV/multimodal model tooling and techniques.
 * Comfort iterating quickly and improving prototypes directly from user feedback.
 * Strong communication skills - honest, empathetic, and transparent with teammates and stakeholders.
 * Willingness to handle the full spectrum of startup engineering tasks (no task is too small).
 * Based in San Francisco or willing to relocate; US work authorization required.

Nice-to-haves:

 * Experience building something that shipped to real users or paying customers.
 * Background or domain knowledge in construction, AEC (architecture/engineering/construction), or related industries.
 * Experience at an early-stage startup or founding team environment.

All experience levels are welcome, including recent graduates with strong production ML portfolios.

## Description

This is a founding engineering role at a seed-stage AI startup building the intelligence layer for the construction industry. The company's platform transforms messy blueprints, permits, and construction documents into actionable insights - accelerating cost estimation and permitting workflows that directly impact housing, hospitals, and schools., * 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.


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