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Clera

Founding Engineer

New York (Remote)$170k–$220kfulltimemidAdded today

About this role

Join an early-stage data infrastructure startup as a founding engineer to build autonomous agents and scalable backend systems for web data extraction and analysis. You'll work directly with the CTO owning full-stack product development, from LLM-powered agents to distributed services handling massive throughput for enterprise customers.

What you'll do

  • Build autonomous agents for information extraction across diverse web environments
  • Design and iterate on full-stack, self-serve applications for non-technical users
  • Scale backend architecture to handle thousands of requests per second and millions of daily rows
  • Contribute across infrastructure as code, ML pipelines, and full-stack development
  • Own significant product areas from concept through production deployment
  • Move fast on a small, early-stage team with high ownership and autonomy

What they're looking for

  • LLM application development and agentic systems (RAG, model evaluation, fine-tuning)
  • Distributed backend systems design and scaling
  • Full-stack development (backend, frontend, infrastructure)
  • Infrastructure as code and deployment automation
  • Web scraping, data extraction, or browser automation at scale
  • JavaScript deobfuscation and reverse engineering (preferred)
  • Antibot systems knowledge (preferred)
  • Production software shipping experience

Benefits

  • Equity in early-stage startup
  • Visa sponsorship available
  • On-site collaboration in New York City
  • High ownership and autonomy in 0-to-1 environment
  • Direct work with CTO on core systems
  • Fast-moving startup pace
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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 application or agentic system you've built—what were the key challenges and how did you solve them?
  • Tell us about your experience scaling a backend system to handle high throughput. What bottlenecks did you encounter and how did you overcome them?
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