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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Build and ship full-stack features for an early-stage AI-powered recruiting platform, working closely with founders and product teams. You'll own features end-to-end—from API design through production—with a focus on integrating LLM capabilities and scaling backend systems for HR tech.

What you'll do

  • Own features from scoping through production deployment with direct impact on core product
  • Design APIs, data models, and implement full-stack code across frontend and backend
  • Build user-facing experiences balancing usability and performance, iterating on feedback
  • Integrate LLM and AI capabilities into reliable, production-ready product flows
  • Design scalable backend services, data schemas, CI/CD pipelines, monitoring, and testing infrastructure
  • Collaborate with founders, product, and design to define priorities and success metrics

What they're looking for

  • TypeScript and React/Next.js
  • Python or Node.js backend development
  • PostgreSQL database design
  • AWS or GCP cloud platforms
  • LLM and AI service integration
  • API and system architecture design
  • Full-stack product development
  • CI/CD and DevOps practices

Benefits

  • Equity and ownership at early-stage growth phase
  • Direct collaboration with founders and leadership
  • Broad technical scope across full stack and infrastructure
  • On-site team environment 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

  • Describe a user-facing feature you shipped end-to-end; what technical decisions did you make and why?
  • Walk us through your experience integrating LLMs or AI services into a production application—what were the reliability challenges?
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