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Clera

Software Engineer

San Francisco$130k–$220kfulltimemidAdded today

About this role

Join a seed-stage AI platform company building intelligent agents for the hospitality industry. You'll own full-stack systems spanning intent classification, knowledge retrieval, tool orchestration, and response generation, with real autonomy in a flat engineering team.

What you'll do

  • Design and build response generation pipelines including intent routers, context assemblers, and tool selectors
  • Develop knowledge retrieval systems that ground agent responses in accurate, current data
  • Build tool orchestration layers handling multi-step workflows with graceful third-party API failure handling
  • Create evaluation frameworks measuring agent quality against real conversations and preventing regressions
  • Implement observability and tracing to make agent reasoning transparent to support teams
  • Optimize system performance and cost through model routing, caching, and prompt refinement

What they're looking for

  • Production system design and debugging
  • AI/ML system architecture (retrieval pipelines, prompt engineering, model orchestration)
  • Backend development (Python, Go, Rust, or Java)
  • Knowledge retrieval and semantic search systems
  • Multi-step workflow and function-calling orchestration
  • Observability and distributed tracing tools
  • Third-party API integration and failure handling
  • Full-stack development across backend, frontend, and infrastructure

Benefits

  • Comprehensive health insurance
  • 401(k) match
  • Equity compensation (0.20% to 1.20%)
  • On-site in San Francisco with flat team structure
  • Autonomy over design decisions and systems ownership
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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 AI/ML system you've built—what went wrong and how did you debug it?
  • Describe your experience building and evaluating retrieval systems. How did you measure quality?
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