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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 terminal for healthcare. You'll own full-stack development across infrastructure, agentic systems, and data pipelines as the third person on the team, directly shaping the platform's architecture and scale.

What you'll do

  • Build and ship full-stack features including frontend UI, real-time systems, and backend infrastructure
  • Design and maintain data pipelines and internal tools for onboarding new financial information sources
  • Improve agentic AI systems for parsing and extracting complex financial data with higher reliability
  • Own projects end-to-end: data inspection, QA, instrumentation, production deployment, and incident response
  • Integrate new financial data sources and build company data ontology in first 30 days
  • Operate production systems with strong observability and debugging practices

What they're looking for

  • Full-stack development (Python/FastAPI backend, TypeScript/React frontend)
  • Data pipeline development and ETL design
  • SQL and relational database management (PostgreSQL)
  • Production LLM and agentic AI application development
  • AWS cloud infrastructure and deployment
  • Real-time or streaming data systems
  • Production observability and debugging
  • Data infrastructure tools (dbt, vector databases)

Benefits

  • Founding engineer equity and impact at seed-stage startup
  • Direct collaboration with founders on core product architecture
  • Full ownership of features from conception to production
  • On-site New York office location
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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 LLM or agentic system you've built—what were the key challenges and how did you handle reliability?
  • Describe your experience building data pipelines at scale. How would you approach onboarding a new financial data source quickly?
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