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Clera

Founding Engineer (AI/ML)

San Francisco$130k–$170kfulltimemidAdded today

About this role

Join an early-stage AI recruiting marketplace as a founding engineer to build the intelligence layer from the ground up. You'll own recommendation systems, ranking algorithms, and LLM-powered matching that directly drive the product's core value, shipping features independently on a rapid cadence.

What you'll do

  • Own the end-to-end AI/ML intelligence layer including recommendation, ranking, and retrieval systems
  • Build and iterate on LLM-powered matching to improve candidate-to-role alignment
  • Ship features from problem definition to production independently with a daily release cadence
  • Architect and maintain production data pipelines and services that power the ML models
  • Collaborate with founding team to translate product goals into shipped systems

What they're looking for

  • Production ML engineering with recommender systems or ranking algorithms
  • LLM APIs and shipping LLM-powered features to users
  • TypeScript or Python
  • PyTorch or TensorFlow
  • Production data pipelines and services
  • Vector search and embeddings (nice to have)
  • React, Supabase, Prisma, or Typesense (nice to have)
  • Strong CS fundamentals and system design

Benefits

  • Competitive founding-hire equity package
  • Salary range: $130,000–$170,000 annually
  • Visa sponsorship available
  • Relocation assistance for candidates outside Bay Area
  • Early-stage impact with high visibility across product
  • Collaborative founding team environment
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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

  • Tell us about a recommender system or ranking algorithm you built in production—what was the business impact and how did you measure it?
  • How have you approached shipping LLM-powered features to real users, and what challenges did you encounter?
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