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Clera

Software Engineer, AI & Data Systems

San Francisco$200k–$280kfulltimemidAdded 2 days ago

About this role

Join a seed-stage AI infrastructure company as a Software Engineer owning high-impact systems across backend, data, and infrastructure. You'll build production AI agents, high-volume data pipelines, and customer-facing products while working directly with founders at significant scale.

What you'll do

  • Design and operate high-volume data pipelines and services with strong reliability and observability
  • Build AI agents, agent harnesses, and developer tools operating at massive scale
  • Ship customer-facing products and core platform capabilities across the full stack
  • Make architecture decisions, document tradeoffs, and iterate as requirements evolve
  • Collaborate directly with founders and customers to translate ambiguous problems into shipped software
  • Use modern AI tools effectively while maintaining strong engineering judgment

What they're looking for

  • Backend development (APIs, data models, distributed systems)
  • Data pipeline design and operations at high volume
  • Production system ownership end-to-end
  • AI-native development (LLMs, agent frameworks)
  • Infrastructure and observability
  • CS fundamentals and system design
  • Identity resolution or data enrichment systems (plus)
  • Full-stack product shipping

Benefits

  • High ownership and direct founder collaboration
  • Work at massive scale with modern AI tools
  • Small, fast-moving team environment
  • On-site in San Francisco
  • Competitive base salary with equity (seed-stage)
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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

  • Tell us about a production system you owned end-to-end—what were the biggest challenges in deployment and ongoing operations?
  • How have you used LLMs or agent frameworks in production? What were the reliability and scaling challenges?
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