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Clera

Founding Engineer

New York (Remote)$156k–$182kfulltimemidAdded today

About this role

Join a Y Combinator-backed startup as founding engineer #3, building AI-native financial research infrastructure for institutional investors. You'll own full-stack development—from data pipelines to frontend—making AI agents reliable and scalable across disparate data sources.

What you'll do

  • Build and maintain data pipelines and internal tooling for onboarding new data sources
  • Improve agentic parsing and extraction systems for production reliability
  • Develop full-stack features across frontend, backend, and real-time systems
  • Own end-to-end delivery: inspect data, QA, instrument, deploy, and debug in production
  • Design and implement company ontology and key product features
  • Optimize database and distributed system performance with observability and monitoring

What they're looking for

  • Python backend development (FastAPI or equivalent)
  • TypeScript and React frontend development
  • Data pipeline and ETL development with SQL
  • PostgreSQL database design and optimization
  • AWS infrastructure and deployment
  • Production observability and distributed systems debugging
  • Agentic AI and LLM application development
  • Real-time data systems and streaming

Benefits

  • Early-stage equity as part of founding team
  • High ownership and influence over product direction
  • Full-stack engineering with varied technical challenges
  • Tight-knit three-person technical team
  • On-site collaboration in New York
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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

  • Describe a production agentic AI or LLM system you built—what were the key reliability challenges and how did you solve them?
  • Walk us through your approach to deciding when to use agents versus deterministic code in a financial data pipeline.
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