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Clera

Founding Engineer

San Francisco$150k–$250kfulltimemidAdded yesterday

About this role

Be the first engineering hire at a funded AI infrastructure startup, architecting and scaling an agent runtime from prototype to production. You'll work directly with the founder across full-stack systems, set technical standards, and grow into a CTO role as the team expands.

What you'll do

  • Architect and rebuild the product into scalable agentic infrastructure supporting millions of users
  • Design and operate agent runtime systems including orchestration, memory, verification, and reliability mechanisms
  • Own end-to-end technical decisions across backend, infrastructure, frontend, and agent systems
  • Establish technical stack, engineering standards, and team culture from the ground up
  • Ship production updates weekly while maintaining reliability for paying customers
  • Scale existing prototype infrastructure to handle real production load and complexity

What they're looking for

  • Production LLM and AI agent systems (not demos or side projects)
  • Full-stack development across backend, infrastructure, and frontend
  • Agent orchestration, memory systems, and multi-agent workflows
  • Scaling architecture from prototype to production infrastructure
  • Systems design and data modeling for high-load services
  • Independent technical decision-making without organizational hierarchy

Benefits

  • Clear path to CTO as team grows
  • Meaningful founding equity
  • Relocation support available
  • Visa sponsorship available
  • Direct founder collaboration and influence on direction
  • Weekly production ship cadence with real business impact
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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 production LLM or AI agent system you built—what were the biggest scaling challenges and how did you solve them?
  • Describe a time you rebuilt or refactored a working prototype into production infrastructure. What architectural decisions had the biggest impact?
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