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Clera

Founding Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join an early-stage AI startup as the first engineering hire outside the founding team. You'll own the complete product stack—from ingesting agent conversation data and detecting failures to generating, verifying, and deploying patches directly to customer repositories through a trusted review interface.

What you'll do

  • Ship end-to-end features across event ingestion, failure detection, patch generation, verification, and customer-facing UI
  • Ensure patch safety and reliability through reproduction logic, guardrails, and checks that prevent faulty PRs from reaching production
  • Move rapidly from customer feedback to shipped solutions without formal approval cycles
  • Engage directly with customers to diagnose system failures and understand trace data
  • Design and refine core architectural decisions around data schemas, service boundaries, and performance tradeoffs
  • Establish engineering standards for testing, on-call rotations, and release processes

What they're looking for

  • TypeScript (full-stack, from frontend to backend services)
  • Event data pipeline design and optimization at scale
  • AI agent development and production debugging
  • Observability, monitoring, and production system troubleshooting
  • Cost-aware infrastructure and per-event efficiency thinking
  • Product sense and independent problem-solving
  • System architecture and design decisions

Benefits

  • Competitive salary ($150–200K annually)
  • Visa sponsorship available
  • Early-stage equity opportunity (implied)
  • On-site collaboration 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

  • Walk us through a production incident you debugged in an AI system—what went wrong and how did you prevent it from happening again?
  • Tell us about a high-volume data pipeline you've built or optimized. How did you think about cost per event?
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