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Clera

Founding Engineer (AI/ML)

San Francisco$130k–$170kfulltimemidAdded today

About this role

Build the core AI/ML intelligence layer for a pre-seed recruiting marketplace, owning recommendation systems, ranking algorithms, and LLM-powered matching from scratch. You'll ship features end-to-end on a daily cadence, working directly with founders to translate product vision into production systems that connect candidates with relevant roles.

What you'll do

  • Own the entire AI/ML intelligence layer including recommendation, ranking, and retrieval systems
  • Build and iterate on LLM-powered candidate-to-role matching that improves alignment measurably
  • Take features from problem definition through production with daily shipping cadence
  • Architect and maintain production data pipelines and ML services
  • Partner with founders to translate product goals into shipped technical systems
  • Establish ML infrastructure and best practices for the early-stage company

What they're looking for

  • Production machine learning (PyTorch, TensorFlow, or equivalent)
  • Recommender systems and ranking algorithms
  • LLM APIs and LLM-powered feature development
  • TypeScript or Python
  • Production data pipelines and services
  • CS fundamentals and system design
  • Vector search and embeddings (nice to have)
  • React, Supabase, Prisma (nice to have)

Benefits

  • Competitive founding-hire equity
  • Visa sponsorship available for eligible candidates
  • Relocation assistance to San Francisco Bay Area
  • Daily shipping and rapid iteration culture
  • Direct partnership with founding team
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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 recommender or ranking system you've built in production—what were the key challenges and how did you measure success?
  • Describe your experience shipping LLM-powered features. How did you approach prompt engineering and integration with LLM APIs?
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