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Clera

AI Engineer

remote (Remote)$80k–$120kfulltimemidAdded today

About this role

Join an early-stage AI-powered talent acquisition startup as a backend-focused AI Engineer. You'll own scalability initiatives and optimize LLM/AI agent pipelines in production, working directly on the systems that power the core product in a high-agency, fast-shipping environment.

What you'll do

  • Design and maintain backend services and infrastructure that scale reliably in production
  • Optimize LLM integrations and AI agent orchestration pipelines for reliability and efficiency
  • Ship features and improvements frequently in close collaboration with a small engineering team
  • Debug and resolve production issues affecting platform performance and user experience
  • Contribute to technical strategy and engineering culture as an early-stage team member
  • Take full ownership of projects from design through deployment

What they're looking for

  • Backend software engineering (5+ years production systems)
  • LLM integration and optimization in production environments
  • AI agent orchestration and workflow design
  • Scalable system architecture and design patterns
  • Production debugging and performance tuning
  • Startup mentality and high-agency ownership
  • End-to-end project delivery
  • Rapid iteration and shipping cadence

Benefits

  • Equity included (negotiable salary/equity mix)
  • Early-stage startup environment with direct impact
  • Small, collaborative engineering team
  • Ownership over meaningful technical challenges
  • Opportunity to shape technical direction
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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.

View all jobs at Clera

Likely interview questions

  • Walk us through a production LLM integration you've built—what were the reliability challenges and how did you solve them?
  • Describe a backend system you designed for scale. What broke first, and how did you fix it?
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