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Clera

Founding Engineer

San Francisco$140k–$220kfulltimemidAdded 2 days ago

About this role

Join an early-stage B2B SaaS startup as the first engineering hire, building AI-powered automation for industrial distributors and manufacturers. You'll own full-stack development across customer discovery, product design, and deployment, shipping features to production customers within your first week.

What you'll do

  • Conduct customer discovery and translate business problems into technical product decisions
  • Build and deploy full-stack features, AI agents, and integrations across the platform
  • Develop and maintain automated testing, evaluation frameworks, and observability tooling
  • Integrate with legacy systems, APIs, and enterprise ERP platforms
  • Own meaningful product areas end-to-end with direct customer impact
  • Improve implementation processes to accelerate customer onboarding and reliability

What they're looking for

  • Full-stack development with TypeScript, Next.js, and Python
  • Production AI agent development with context engineering and evaluation
  • Google Cloud Platform deployment and management
  • Legacy system and ERP integration experience
  • Customer discovery and requirements gathering
  • Testing, observability, and automated evaluation frameworks
  • Rapid shipping and startup execution
  • LLM frameworks and AI agent orchestration

Benefits

  • Visa sponsorship available
  • On-site work in San Francisco
  • First engineering hire with significant equity and autonomy
  • Direct customer interaction and product impact
  • Early-stage startup environment with rapid iteration
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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 agent you built—how did you design the context, tools, and evaluation framework?
  • Describe a time you shipped a feature to production quickly without sacrificing quality. What trade-offs did you make?
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