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Clera

Founding Engineer (Applied AI)

San Francisco$120k–$180kfulltimemidAdded today

About this role

Join an early-stage construction tech startup as a Founding Engineer to build production AI systems that automate blueprint analysis, material estimation, and cost prediction. You'll own the computer vision and ML intelligence layer, work directly with founders, and ship prototypes that solve real construction problems from day one.

What you'll do

  • Develop and deploy computer vision models for blueprint and document understanding in construction workflows
  • Build end-to-end AI systems integrating object detection, LLMs, and material quantification
  • Ship working prototypes rapidly and refine based on user feedback
  • Orchestrate multiple ML models to solve complex estimation problems
  • Handle foundational engineering work critical to early-stage product development
  • Collaborate with founders on technical and product strategy

What they're looking for

  • Computer Vision (object detection, image processing)
  • Deep Learning and Machine Learning
  • Python
  • ML/CV frameworks and tooling (PyTorch, TensorFlow, etc.)
  • Production AI systems deployment
  • Document understanding and LLMs
  • Prototyping and iterative development
  • Cross-functional communication

Benefits

  • Early-stage equity
  • Competitive salary ($120k–$180k)
  • Full-time permanent position
  • Direct influence on product and technical direction
  • Work on high-impact construction automation problems
  • In-office collaboration with founders in San Francisco
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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.

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Likely interview questions

  • Walk us through a computer vision or ML system you shipped to production—what were the biggest technical and operational challenges?
  • How do you approach rapid prototyping when requirements are ambiguous or evolving?
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