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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Join an early-stage AI platform startup as a mid-level product engineer to own features end-to-end, from design through production. You'll build full-stack capabilities integrating LLMs into real product flows while working closely with founders and shaping technical direction in a high-ownership, fast-moving environment.

What you'll do

  • Own end-to-end product features from requirements through production deployment
  • Design and implement APIs, data models, frontend (React/Next.js), and backend services
  • Integrate LLM and AI capabilities into product workflows for practical user impact
  • Build and maintain scalable backend systems with production-grade monitoring and observability
  • Ship user-focused experiences iterating on feedback and performance metrics
  • Collaborate with founders and cross-functional teams on roadmap and success metrics

What they're looking for

  • TypeScript and React/Next.js
  • Python or Node.js backend development
  • PostgreSQL database design and optimization
  • AWS or GCP cloud platforms
  • LLM and AI service integration
  • CI/CD, monitoring, observability, and automated testing
  • Full-stack product development
  • Product sense and user-centric design

Benefits

  • Early-stage equity opportunity
  • Competitive salary $150,000–$300,000 annually
  • On-site collaboration with founding team in San Francisco
  • High ownership and direct influence on product direction
  • Opportunity to shape architecture at early stage
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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 recent end-to-end product feature you shipped—from discovery to launch and iteration.
  • Tell us about your experience integrating LLMs or AI services into production applications. What challenges did you encounter?
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