Clera
Founding Engineer (Applied AI)
San Francisco$100k–$180kfulltimemidAdded today
About this role
Join an early-stage construction tech startup as a Founding Engineer to build AI systems that automate blueprint reading and material extraction. You'll focus on computer vision and LLM models applied to real construction documents, shipping prototypes rapidly and iterating with users.
What you'll do
- Develop and deploy computer vision and deep learning models for blueprint understanding and material extraction
- Design and coordinate a model-garden architecture for handling diverse construction documents
- Build object detection models and supporting ML production pipelines
- Rapidly prototype AI solutions and iterate based on direct user feedback
- Implement LLM and AI techniques tailored to construction industry problems
What they're looking for
- Computer Vision (object detection, document analysis)
- Deep Learning / Machine Learning
- Python
- Production ML systems and MLOps
- Large Language Models (LLMs)
- Model deployment and inference optimization
- Technical communication and cross-functional collaboration
Benefits
- Competitive salary: $100K–$180K USD
- Founding engineer equity stake and early-stage growth opportunity
- Focus on high-impact infrastructure and housing accessibility problems
- Autonomy to tackle hard technical problems and ship quickly
- Direct collaboration with users and founders
Opens the official application on the employer’s site. No login required.
Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Can you walk us through a computer vision or ML system you shipped to production—what was the hardest part?
- How do you approach building and iterating on models when initial assumptions are wrong?