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Campfire

AI Engineer - Agents

San Francisco$180k–$300kfulltimemidAdded today

About this role

Campfire seeks a Staff AI Engineer to design and operate production AI agents that automate accounting workflows for startups and mid-market tech companies. You'll lead agent architecture development, ship customer-facing features, and establish reliability practices while balancing software engineering excellence with hands-on AI implementation.

What you'll do

  • Design and build production AI agents that perform real accounting tasks using tool calling, orchestration, and data retrieval
  • Lead agent architecture decisions and establish engineering practices that ensure reliability and quality in production
  • Own agent quality through evaluations, tracing, monitoring, and systematic debugging of production failures
  • Collaborate with product and design to translate ambiguous requirements into trustworthy customer-facing features
  • Mentor engineers and raise engineering standards while remaining actively involved in coding
  • Balance tradeoffs between accuracy, latency, cost, and complexity to choose appropriate solutions

What they're looking for

  • Python and backend systems design
  • LLM-powered agent development and deployment
  • Distributed systems and data modeling
  • Production monitoring, evaluation, and observability
  • Agent permissions, data access control, and failure recovery
  • Staff-level technical leadership and mentorship
  • API design and testing practices
  • Financial workflows and domain understanding (plus)
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Campfire

Campfire builds accounting software designed for startups and mid-market tech companies, with a focus on automating financial workflows through intelligent features. The company is hiring AI engineers and full-stack engineers to develop and deploy machine learning solutions and modern platform capabilities in their San Francisco office.

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Likely interview questions

  • Can you walk us through a production AI agent system you built? What were the key reliability and quality challenges you faced?
  • How do you approach evaluating and monitoring agent performance in production, especially around accuracy and failure modes?
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