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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Mid-level full-stack product engineer opportunity at an early-stage AI recruiting startup. You'll own features end-to-end, integrating LLMs into core product flows while working closely with a small founding team to establish the technical foundation.

What you'll do

  • Own product features from design through deployment, including API design, data modeling, and full-stack implementation
  • Build and ship user-facing experiences that balance usability and performance with iterative refinement
  • Integrate LLMs and AI services into production flows reliably and with user focus
  • Design and implement scalable backend services, data schemas, and operational tooling (CI/CD, monitoring, testing)
  • Collaborate directly with founders, product, and design to prioritize work and define success metrics
  • Participate in architecture and roadmap planning, making pragmatic tradeoffs for early-stage growth

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
  • Monitoring, observability, and CI/CD pipelines
  • Multi-tenant and enterprise systems design
  • Full-stack product engineering

Benefits

  • Early-stage equity opportunity
  • Direct collaboration with founding 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.

View all jobs at Clera

Likely interview questions

  • Tell us about a user-facing product feature you shipped end-to-end and how you measured its success.
  • Describe your hands-on experience integrating LLMs or AI services into production. What challenges did you encounter?
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