Clera
Founding AI Engineer
About this role
Join an early-stage construction tech startup as a founding AI engineer to build computer vision and multimodal AI systems that transform construction documents into cost estimates and workflows. You'll own the full intelligence layer—from modeling through production deployment—working on real-world document understanding with direct impact on housing and infrastructure projects.
What you'll do
- Apply computer vision, LLM, and multimodal AI techniques to construction documents and blueprints
- Rapidly prototype and iterate AI models based on user feedback and production data
- Build and deploy end-to-end AI systems handling noisy, heterogeneous document inputs
- Own data pipelines, model training, and inference infrastructure
- Execute practical engineering work including data labeling, ETL, and deployment
- Collaborate with product and users to identify and solve high-impact problems
What they're looking for
- Computer vision and image processing
- Large language models (LLMs) and multimodal AI
- Production ML systems and deployment
- Data pipeline engineering and ETL
- Document understanding and OCR
- Python and ML frameworks (PyTorch, TensorFlow)
- Rapid prototyping and iteration
- Cross-functional collaboration and communication
Benefits
- Equity as part of founding-team package
- On-site location in San Francisco
- Work on high-impact infrastructure problems
- Early-stage startup autonomy and influence
- Opportunity to shape product and engineering culture
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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.
View all jobs at CleraLikely interview questions
- Describe a production ML system you built from scratch—what were the biggest challenges with data quality or model deployment?
- How have you handled iterating on AI models when real-world data didn't match your training assumptions?