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Clera

Founding Software Engineer

San Francisco$130k–$200kfulltimemidAdded 1 month ago

About this role

Join a well-funded seed-stage B2B SaaS startup to build an agentic supply chain integrations platform. As a founding engineer, you'll own full-stack development across frontend, backend, and infrastructure while shaping the product direction and engineering culture from day one.

What you'll do

  • Design and develop the integrations platform including mapping, testing, retries, queues, and observability
  • Build features across the full tech stack: React/TypeScript frontend, Node.js backend, and AWS infrastructure
  • Own end-to-end project delivery from specification through implementation to customer impact
  • Implement LLM and agentic workflow integrations
  • Define product direction and engineering culture as an early team member
  • Build and maintain distributed systems with strong reliability patterns

What they're looking for

  • TypeScript and React
  • Node.js
  • AWS cloud infrastructure
  • PostgreSQL
  • Large Language Models (LLMs) and agent-based platforms
  • Observability and distributed tracing
  • Full-stack development
  • Computer science fundamentals

Benefits

  • Salary: $130,000–$200,000 USD annually
  • Early-stage equity as a founding team member
  • Visa sponsorship available
  • High ownership and impact on company trajectory
  • On-site role in San Francisco
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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 product you've built from 0→1, either at a startup or as a side project. What was your role across frontend, backend, and infrastructure?
  • Describe your experience integrating with or building workflows around LLMs or agent-based platforms. What challenges did you encounter?
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