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Clera

Founding Engineer – Full Stack

San Francisco$150k–$250kfulltimemidAdded today

About this role

Join a Y Combinator-backed AI startup as a Founding Engineer building brand intelligence tools that leverage LLMs. You'll own full-stack development from customer insights through infrastructure, shaping product and engineering culture at the ground level of a new category of AI software.

What you'll do

  • Own end-to-end problems spanning customer research, product design, and infrastructure implementation
  • Maintain direct feedback loops with customers and executives to ship features rapidly
  • Build agentic systems and LLM-powered capabilities in production environments
  • Help establish engineering practices and culture for an early-stage founding team
  • Translate ambiguous customer needs into concrete product features with strong design instincts
  • Develop full-stack solutions across React frontends, Python backends, and AWS infrastructure

What they're looking for

  • React
  • Python
  • Terraform
  • AWS
  • LLM/agentic systems development
  • Full-stack architecture
  • Product sense and design thinking
  • Customer discovery and feedback

Benefits

  • Competitive salary: $150K–$250K USD
  • Early-stage equity as founding team member
  • Visa sponsorship available
  • Shape engineering culture from inception
  • Work on emerging AI-powered software category
  • Direct influence on product direction
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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

  • Can you walk us through a recent project where you built an end-to-end feature from customer need to production infrastructure?
  • Tell us about your experience building or deploying LLMs or agentic systems in production—what challenges did you face?
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