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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Join an early-stage AI recruitment startup as a mid-level full-stack product engineer, owning end-to-end features from design through production. You'll build intelligent talent-matching experiences using TypeScript, React, Python, and LLM integrations while collaborating directly with founders in a fast-moving, high-ownership environment.

What you'll do

  • Own full-cycle product features from requirements and API design through production deployment
  • Build performant frontend experiences in TypeScript and React/Next.js
  • Design and implement backend services, data schemas, and operational tooling including CI/CD and monitoring
  • Integrate LLM and AI capabilities into product flows for reliable end-user value
  • Collaborate with founders, product, and design to define priorities and success metrics
  • Contribute to architecture and roadmap decisions with pragmatic tradeoffs for early-stage growth

What they're looking for

  • TypeScript and React/Next.js
  • Backend development (Python or Node.js)
  • PostgreSQL and relational database design
  • AWS or GCP cloud platforms
  • LLM and AI service integration
  • CI/CD pipelines and DevOps tooling
  • Product thinking and user-centric problem solving
  • Full-stack system design

Benefits

  • Broad technical ownership across frontend, backend, and DevOps
  • Direct collaboration with founders and influence on product direction
  • Early-stage equity upside potential
  • On-site work environment in San Francisco
  • Opportunity to ship high-impact AI-driven features
  • Learning from building AI-powered products in production
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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 user-facing feature you shipped end-to-end—what was the toughest technical or product decision?
  • Describe your experience integrating LLMs into production applications. What went wrong, and how did you fix it?
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