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Clera

Founding Engineer (AI/ML)

San Francisco$130k–$170kfulltimemidAdded today

About this role

Join this early-stage AI recruiting marketplace as a founding engineer to build the core intelligence layer connecting talent with opportunities. You'll own recommendation systems, LLM-powered matching algorithms, and production data pipelines while shipping features on a rapid cadence alongside the founding team.

What you'll do

  • Build and own recommendation systems and ranking algorithms that power candidate-role matching
  • Develop LLM-powered features and integrate them into production
  • Design and maintain production-grade data pipelines and backend services
  • Translate product goals into shipped, end-to-end features
  • Collaborate cross-functionally with founding team on feature development
  • Operate in a fast-paced startup environment with daily shipping cycles

What they're looking for

  • Large Language Models (LLM APIs and production implementations)
  • Recommendation systems and ranking algorithms
  • TypeScript and React
  • Supabase and Prisma
  • Database design and optimization
  • Typesense or similar search infrastructure
  • trigger.dev or workflow automation tools
  • End-to-end product engineering

Benefits

  • Founding-level ownership of core AI/ML systems
  • Work directly with founding team
  • Visa sponsorship available
  • Hybrid work in San Francisco
  • Rapid iteration and daily shipping cadence
  • Opportunity to shape product direction early
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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.

View all jobs at Clera

Likely interview questions

  • Can you walk us through a production LLM-powered feature you built—how did you handle latency, accuracy, and cost trade-offs?
  • Tell us about a recommendation or ranking system you built. How did you validate that it was working?
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