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Clera

Founding Engineer (AI/ML)

San Francisco$130k–$170kfulltimemidAdded 3 days ago

About this role

Join a pre-seed AI recruiting marketplace as a founding engineer to architect and own the core AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. You'll ship features daily in a fast-paced startup environment, taking products from concept to production while working directly with the founding team.

What you'll do

  • Own and architect the full AI/ML intelligence layer covering recommendations, ranking, and retrieval systems
  • Build and iterate on LLM-powered matching to improve candidate-role alignment metrics
  • Take features from problem definition through production on a rapid shipping cadence
  • Design and maintain production data pipelines and ML services that power the models
  • Collaborate with founders to translate product goals into shipped technical systems
  • Debug and optimize model performance and system reliability in production

What they're looking for

  • Production ML systems and recommender systems architecture
  • Python or TypeScript (required proficiency in at least one)
  • LLM APIs and LLM-powered feature development
  • Data pipeline design and production ML services
  • Ranking algorithms and retrieval systems
  • CS fundamentals and startup execution
  • Vector search and embeddings (nice to have)
  • React, Supabase, Prisma, or similar modern tools (nice to have)

Benefits

  • Founding-hire equity package
  • Competitive salary ($130,000–$170,000 annually)
  • Visa sponsorship available
  • Relocation assistance provided
  • Opportunity to architect core product as first ML hire
  • Fast-paced startup environment with direct founder 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

  • Describe a recommender system or ranking algorithm you built and shipped to production—what were the key technical challenges and how did you measure success?
  • Walk us through your experience integrating LLM APIs into a production feature. How did you handle latency, cost, or quality concerns?
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