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Clera

Founding Product Engineer

San Francisco$140k–$180kfulltimemidAdded today

About this role

Join an early-stage legal AI startup as a founding product engineer to build core features from scratch, working across the full stack while partnering with attorneys and AI specialists. This high-ownership role demands hands-on full-stack development, LLM integration, and rapid iteration in an intense, six-day-per-week environment.

What you'll do

  • Build and own full-stack product features from data models to attorney and client-facing interfaces
  • Partner with AI engineers and licensed attorneys to translate legal workflows into production software
  • Make pragmatic architecture decisions balancing speed with long-term scalability
  • Write maintainable, reliable code and take complete ownership of production systems
  • Define engineering culture, coding standards, and hiring practices as the team grows
  • Work directly with domain experts to implement legal judgment into high-quality software

What they're looking for

  • TypeScript and React frontend development
  • Modern backend stack (language not specified)
  • Relational database design and optimization
  • AI and LLM integration (professional or project experience)
  • Full-stack feature ownership and end-to-end delivery
  • DevOps, CI/CD, and production monitoring
  • Strong engineering judgment and technical trade-offs
  • Communication and product instincts

Benefits

  • Founding engineer equity and early-stage impact
  • Direct influence on engineering culture and standards
  • Hands-on work with AI and legal tech innovation
  • High ownership and autonomy over product direction
  • San Francisco location with on-site team collaboration
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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 full-stack feature you shipped end-to-end—what were the key technical trade-offs you made under time pressure?
  • Describe your experience building with LLMs or AI—what worked, what didn't, and how did you handle production reliability?
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