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Clera

Founding Product Engineer

San Francisco$200k–$250kfulltimemidAdded 2 days ago

About this role

Join this early-stage B2B SaaS startup as a Founding Product Engineer to own full product modules in an AI-driven pricing and monetization platform. You'll work directly with customers and leadership to design agentic AI systems, backend services for financial correctness, and shape the engineering culture of a high-output founding team.

What you'll do

  • Scope, design, and build entire product modules end-to-end in collaboration with customers
  • Design architecture for agentic AI systems including conversational interfaces, web UIs, and vendor integrations
  • Migrate pricing and business logic from client to backend services using FastAPI and Python
  • Extend and maintain a typed pricing ontology using Pydantic models for SKUs, propositions, and pricing structures
  • Lead architectural reviews and establish standards for testing, observability, and performance monitoring
  • Mentor team members and shape product strategy and engineering best practices

What they're looking for

  • Backend development with FastAPI and Python
  • Full-stack engineering with React and TypeScript
  • MongoDB or similar database systems
  • LLM integration and agentic AI workflows
  • B2B SaaS monetization concepts (pricing, packaging, CPQ)
  • API design and versioning
  • Financial systems and correctness-critical logic
  • System architecture and technical leadership

Benefits

  • Base salary $200,000–$250,000 annually
  • Founding-level ownership of product and engineering culture
  • Direct customer collaboration and product strategy influence
  • Work on cutting-edge agentic AI systems
  • High-autonomy role with minimal process overhead
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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 time you owned a full product module from ambiguous customer problem to shipped feature without a dedicated PM—what was your process?
  • Describe your experience building or integrating LLM-driven features; what were the key architectural challenges?
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