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Clera

Founding Engineer

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

About this role

Join a three-person Y Combinator-backed startup as a founding engineer building an AI-native financial research platform for healthcare investors. You'll own the full stack—frontend, backend, data pipelines, and agentic AI systems—shipping core features from day one in a fast-moving, early-stage environment.

What you'll do

  • Ship full-stack features across frontend (React/TypeScript), backend (Python/FastAPI), and infrastructure
  • Design and optimize data pipelines and ETL systems to ingest and process disparate data sources
  • Improve agentic AI parsing and extraction systems for reliability and speed
  • Build internal tooling that enables analysts to add new sources and maintain sector coverage
  • Own end-to-end accountability: data inspection, QA, instrumentation, deployment, and production support
  • Architect company data ontology and core product features like the earnings data center

What they're looking for

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

Benefits

  • Early equity stake as a founding team member
  • Direct influence over product direction and company strategy
  • Full ownership of meaningful, production systems from day one
  • On-site collaboration with founding team in New York
  • Exposure to capital markets and financial research domain
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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 built—what worked, what didn't, and how you debugged it?
  • Describe a time you owned a feature end-to-end from data to shipping. What was the hardest part?
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