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Clera

Founding Engineer

San Francisco$120k–$250kfulltimemidAdded 1 month ago

About this role

Join a YC-backed enterprise AI startup as a Founding Engineer to build agentic systems at scale. You'll own full-stack development across backend (Go), frontend (React/TypeScript), and AI infrastructure, working autonomously to ship products that solve real enterprise problems.

What you'll do

  • Develop and optimize agentic systems for accuracy, reliability, and enterprise-scale deployment
  • Own features end-to-end from user research through production, with minimal guidance
  • Build full-stack services using Go backend and React/TypeScript frontend
  • Engage with users to understand pain points and scope valuable product solutions
  • Implement CI/CD pipelines, automated testing, and production monitoring systems
  • Champion AI-first development practices and code quality across the team

What they're looking for

  • Go (Golang) backend development
  • React and TypeScript frontend development
  • Agentic systems and LLM engineering
  • REST/gRPC APIs and microservices architecture
  • CI/CD, automated testing, and deployment workflows
  • Cloud platforms (AWS/GCP) and Docker/Kubernetes
  • Relational and NoSQL database design
  • Full-stack product thinking and rapid iteration

Benefits

  • Salary: $120,000–$250,000 USD annually
  • Generous equity with outsized founding-level impact
  • Unlimited AI tooling budget
  • Direct influence on product direction and company strategy
  • Work with proven product-market fit and rapidly growing revenue
  • On-site collaboration in San Francisco with high-output founding team
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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 Go backend service you built end-to-end. What were the key architectural decisions, and how did you handle scaling or reliability challenges?
  • Describe your experience building agentic AI systems or working with LLMs in production. What's one challenge you solved around agent reliability or accuracy?
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