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Clera

Founding Engineer

San FranciscoFrom $150kfulltimemidAdded today

About this role

Be the first engineering hire at an early-stage AI/ML startup building automated agent failure detection and patch generation tools. You'll own the complete system from detection through patch verification, set engineering standards, and work directly with customers to ship end-to-end solutions.

What you'll do

  • Ship end-to-end features from failure detection through patch generation and verification
  • Own the trust and safety surface including reproduction logic, guardrails, and PR verification checks
  • Move independently from customer needs to shipped solutions without external approval
  • Engage directly with customers to diagnose issues and surface problems early
  • Make long-term architectural decisions on schema design and service boundaries
  • Define engineering culture including testing standards, on-call practices, and release criteria

What they're looking for

  • TypeScript (full stack: frontend, backend, and core services)
  • Production AI agent development and deployment
  • High-volume data pipeline design with cost optimization
  • Architectural and systems design
  • Product thinking and problem-solving without detailed specs
  • Observability and production monitoring
  • PR automation or code quality tooling (bonus)

Benefits

  • Visa sponsorship available
  • Shape company engineering culture from the ground up
  • Direct customer interaction and impact
  • Full ownership of critical system components
  • Work with cutting-edge AI/ML technology
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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 shipped a complex end-to-end feature independently—what was your decision-making process?
  • Tell us about your experience building AI agents in production. What surprised you most about how they failed?
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