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Clera

Software Engineer, AI & Data Systems

San Francisco$200k–$220kfulltimemidAdded yesterday

About this role

Join a seed-stage people data and AI company as a Software Engineer to own full-stack systems from concept to production at scale. You'll build customer-facing products, operate high-volume data pipelines, and develop AI agents while collaborating closely with founders on ambiguous problems.

What you'll do

  • Build and ship customer-facing products across backend, data, infrastructure, and frontend
  • Design and operate high-volume data pipelines and services with reliability and security
  • Develop AI agents and agent harnesses, then operate them reliably at large scale
  • Make architecture decisions and explain tradeoffs as requirements evolve
  • Collaborate with founders and customers to translate vague requirements into working software
  • Use modern AI development tools while maintaining strong engineering judgment

What they're looking for

  • Backend engineering (APIs, data models, distributed systems)
  • Production system ownership from concept through operations
  • High-volume data pipeline design and operations
  • Computer science fundamentals
  • Full-stack product development
  • LLMs and agent frameworks (preferred)
  • Identity resolution or data matching systems at scale (preferred)
  • Early-stage startup experience

Benefits

  • Equity participation in seed-stage company
  • Full-time, on-site role in San Francisco
  • Direct collaboration with founders
  • High ownership and end-to-end responsibility
  • Work on cutting-edge AI and data systems
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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 production system you owned end-to-end—what were the key challenges in going from concept to reliable operation?
  • Describe your experience building or operating high-volume data pipelines. What scale did you work at, and how did you ensure reliability?
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