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Clera

Founding Engineer

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

About this role

Join a Y Combinator-backed AI fintech startup as a founding engineer building an AI-native financial research platform for healthcare sector investors. You'll own the full stack—frontend, backend, and infrastructure—on a small team, shipping production systems that serve institutional clients from day one.

What you'll do

  • Develop full-stack features across frontend (React/TypeScript), backend (Python/FastAPI), and real-time systems
  • Design and build data pipelines and ETL systems enabling analysts to integrate new data sources quickly
  • Improve agentic AI systems for parsing and extraction, balancing agent reasoning with deterministic logic
  • Own production systems end-to-end: data inspection, QA, monitoring, deployment, and incident response
  • Build internal tools and company data ontology to accelerate sector coverage and reliability
  • Integrate new data sources and ship core product modules within the first 30 days

What they're looking for

  • Python backend development (FastAPI or equivalent)
  • TypeScript and React frontend development
  • Data pipeline and ETL design
  • PostgreSQL and relational database optimization
  • AWS cloud infrastructure and deployment
  • Production observability, monitoring, and debugging
  • LLM and agentic AI systems in production
  • Real-time data systems and streaming

Benefits

  • Founding engineer equity and impact on product direction
  • Full-stack ownership with minimal bureaucracy
  • Work with a small, fast-moving team at a seed-stage startup
  • Base salary $156,000–$182,000 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

  • Describe a production LLM or agentic system you built—what went wrong, and how did you debug it?
  • Walk us through your approach to designing a data pipeline that analysts can extend with new sources without engineering help.
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