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Clera

Product Engineer (Mid-Level)

San Francisco$150k–$300kfulltimemidAdded today

About this role

Join an early-stage AI recruiting startup as a mid-level full-stack engineer owning product features end-to-end. You'll work directly with founders to build and scale core product, integrating LLM capabilities and shipping user-facing experiences in a fast-paced, high-ownership environment.

What you'll do

  • Own complete product features from scoping through deployment, iteration, and production support
  • Design APIs, data models, and implement both frontend (React/Next.js) and backend (Python/Node.js) code
  • Build and optimize user-facing experiences, balancing usability with performance based on metrics
  • Integrate LLM and AI capabilities into product flows to deliver reliable intelligence to end users
  • Design scalable backend services, implement CI/CD pipelines, monitoring, and automated testing infrastructure
  • Collaborate with founders, product, and design teams on prioritization, roadmap, 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 and AI service integration (production-focused)
  • CI/CD pipelines, monitoring, logging, and observability
  • API design and RESTful architecture
  • Full-stack product development and shipping

Benefits

  • Work directly with founders on core product strategy and technical direction
  • High ownership across frontend, backend, and DevOps in a small team
  • Opportunity to shape architecture and roadmap at early stage
  • On-site location in San Francisco
  • Competitive salary range of $150,000–$300,000 USD annually
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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

  • Tell us about a product feature you owned end-to-end—what was the technical scope and how did you measure success?
  • Describe your experience integrating LLMs or AI services into production systems. What were the challenges?
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