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Clera

Founding Engineer

New York (Remote)$170k–$220kfulltimemidAdded today

About this role

A founding engineer role at a seed-stage data infrastructure startup building autonomous agents and distributed systems for web data extraction at enterprise scale. You'll work directly with the CTO across the full stack—from LLM-powered agents to backend services handling thousands of requests per second—with significant product ownership and autonomy.

What you'll do

  • Develop autonomous agents for web information extraction in unpredictable environments
  • Design and iterate on full-stack, self-serve applications for non-technical users
  • Scale and maintain backend architecture processing thousands of requests per second and millions of data rows daily
  • Build infrastructure as code and deployment automation systems
  • Contribute to ML pipelines and LLM-based features
  • Ship features across the entire stack in rapid iteration cycles

What they're looking for

  • Full-stack software development (backend, frontend, infrastructure)
  • LLM applications and agentic systems (RAG, fine-tuning, model evaluation)
  • High-scale distributed systems design
  • Infrastructure as code and deployment automation
  • Web scraping, web automation, or data extraction
  • JavaScript deobfuscation or reverse engineering
  • Browser automation and antibot systems
  • Production software shipping in early-stage environments

Benefits

  • Significant equity stake as a founding engineer
  • Visa sponsorship available
  • Collaborative work with CTO and founding team
  • Autonomy to own large product areas
  • Fast-paced startup environment with rapid iteration
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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

  • Describe your experience building LLM applications or agentic systems—what was most challenging about RAG implementation or fine-tuning in production?
  • Walk us through how you've scaled a backend system to handle thousands of requests per second; what bottlenecks did you encounter?
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