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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Join an early-stage AI recruiting platform as a mid-level full-stack Product Engineer to build end-to-end features that connect talent with startups. You'll own features from conception through production, integrate LLM capabilities into core workflows, and collaborate closely with founders to shape technical direction as the company scales.

What you'll do

  • Own full-stack product features from requirements through production deployment and iteration
  • Design APIs and data models while implementing both frontend and backend code
  • Integrate LLM and AI capabilities into core product flows for reliable user experiences
  • Build and maintain scalable backend services, CI/CD pipelines, monitoring, and automated testing
  • Ship user-facing experiences that balance usability and performance based on feedback
  • Collaborate with founders, product, and design to define priorities and success metrics

What they're looking for

  • TypeScript and React/Next.js (frontend)
  • Python or Node.js (backend)
  • PostgreSQL database design and optimization
  • AWS or GCP cloud platforms
  • LLM/AI service integration (production-focused)
  • CI/CD, monitoring, and observability
  • Full-stack product ownership and shipping
  • Automated testing and quality assurance

Benefits

  • Equity compensation (early-stage)
  • Ability to shape technical direction and architecture
  • Direct collaboration with founders and small team
  • On-site in San Francisco
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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 full-stack feature you shipped end-to-end—what was your design process and how did you measure success?
  • Describe a time you integrated an LLM or AI service into production. What challenges did you face around reliability and latency?
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