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Clera

Founding Engineer

San Francisco$140k–$220kfulltimemidAdded today

About this role

Join an early-stage B2B SaaS startup as the first engineering hire, building automation tools for back-office workflows in manufacturing and distribution. You'll own full-stack development across customer-facing features, AI agents, and integrations while working directly with live customers to ship impactful products from day one.

What you'll do

  • Conduct customer discovery to understand workflows and translate pain points into product features
  • Build full-stack features using TypeScript, Next.js, and Python across frontend, backend, and infrastructure
  • Design and deploy AI agents with context engineering, tool integration, and evaluation frameworks
  • Integrate with legacy systems, ERPs, CRMs, and third-party enterprise platforms
  • Develop testing, evaluation frameworks, and observability tooling for production reliability
  • Own meaningful product surface areas and drive features independently from design through deployment

What they're looking for

  • TypeScript and Next.js
  • Python
  • Google Cloud Platform (GCP)
  • AI agent development and LLM frameworks
  • Full-stack web development
  • Enterprise API integration and legacy system connectivity
  • Automated testing and evaluation frameworks
  • Customer discovery and requirements gathering

Benefits

  • Visa sponsorship available
  • Early-stage equity opportunity (implied)
  • Outsized impact on product direction and company growth
  • Direct customer engagement and relationship building
  • Opportunity to own substantial technical areas
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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

  • Describe a time you built a production AI agent—how did you approach context engineering and tool design?
  • Walk us through an example where you integrated your application with a legacy enterprise system. What were the main challenges?
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