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Clera

Founding Engineer ($3M pre-seed)

Denver (Remote)fulltimemidAdded 1 month ago

About this role

Join Clera as engineer #4 to build AI-powered recruiting automation that's disrupting a $600B industry. You'll have full ownership of product areas, ship daily, and work directly with founders in a San Francisco hackerhouse environment with early-stage traction and top-tier backing.

What you'll do

  • Own entire product features from concept through production deployment
  • Build and iterate on AI agent systems that automate recruiting workflows
  • Collaborate directly with CTO and founding engineers on technical direction
  • Engage with customers to gather feedback and drive product decisions
  • Ship code rapidly while balancing speed with scalability and quality
  • Contribute to core platform architecture using TypeScript, React, and LLM APIs

What they're looking for

  • TypeScript and React development
  • Practical AI/LLM API integration experience
  • Full-stack software engineering
  • AI-assisted coding tools (Cursor)
  • High ownership and agency mindset
  • Customer-focused iteration
  • Startup adaptability and resourcefulness

Benefits

  • Significant equity as early founding engineer
  • Competitive salary
  • San Francisco hackerhouse living and working arrangement
  • Direct impact on product strategy and company direction
  • Visa sponsorship support
  • Work alongside experienced founders and A-player team
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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 Clera

Likely interview questions

  • Walk us through a project where you built with AI tools like Cursor. How did it change your development workflow, and what were the tradeoffs?
  • Describe your experience working with LLM APIs in production. What challenges did you face integrating them into a real product?
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