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Clera

Founding Agentic Engineer

San Francisco$150k–$300kfulltimemidAdded today

About this role

Founding-level engineering role building production agentic systems for a gig economy platform. You'll design and own the entire agent layer end-to-end, establish engineering standards, and work directly with the CTO to scale AI-driven labor marketplace operations.

What you'll do

  • Design and own agent layer holistically: context engineering, self-learning systems, evals, and agent harness architecture
  • Build knowledge base systems enabling each new agent integration to improve upon the last
  • Develop self-healing workflows with autonomous failure detection, diagnosis, and recovery
  • Establish engineering patterns and standards for how all company agents are built and measured
  • Grow the agentic engineering team and mentor future team leads
  • Partner with founding team to experiment on core agentic systems

What they're looking for

  • Agentic software development with recent production shipping experience
  • Harness-centric agent development and eval-driven iteration
  • Agent tracing and evaluation frameworks
  • TypeScript proficiency
  • Effect-TS (preferred)
  • PostgreSQL
  • LLM and agent tooling
  • Production systems engineering at scale

Benefits

  • Founding-level equity opportunity (implied)
  • Work directly with CTO on core technology decisions
  • Visa sponsorship available
  • On-site in San Francisco
  • Career path toward leading engineering teams
  • Impact on real-world labor marketplace operations
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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 recent agentic system you shipped to production—what were the hardest eval problems you solved?
  • How do you approach context engineering and what evaluation frameworks have you used to iterate on agent behavior?
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