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Clera

Founding Engineer (AI/ML)

San Francisco$130k–$170kfulltimemidAdded today

About this role

A pre-seed AI recruiting marketplace seeks a Founding Engineer to build the core ML intelligence layer, including recommendation systems and LLM-powered matching that connects candidates with opportunities. You'll own the full stack from data pipelines to production features in a fast-moving, well-funded startup.

What you'll do

  • Build and own the AI/ML intelligence layer including recommendation systems and ranking algorithms
  • Develop LLM-powered matching features to improve candidate–role alignment
  • Architect and maintain production-grade data pipelines and backend services
  • Collaborate cross-functionally to translate product goals into shipped features
  • Deliver end-to-end features with full ownership on a daily shipping cadence
  • Design and manage database schemas using Supabase and Prisma

What they're looking for

  • 3+ years shipping production software features end-to-end
  • LLM APIs and production LLM-powered feature development
  • Recommendation systems, ranking algorithms, or ML-powered matching
  • TypeScript and React in production
  • Supabase, Prisma, and production database design
  • Production data pipeline and service development
  • Typesense search/ranking infrastructure
  • trigger.dev or similar workflow automation tools

Benefits

  • Foundational equity opportunity at pre-seed stage
  • Visa sponsorship available
  • Hybrid/in-person collaboration in San Francisco
  • High ownership and autonomy as founding engineer
  • Access to notable VC-backed investors and angels
  • Fast-paced, scrappy startup environment with daily shipping
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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 LLM-powered feature you've built—what were the key technical challenges and how did you solve them?
  • Describe your experience building recommendation or ranking systems. How did you measure and optimize their performance?
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