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Clera

AI Engineer

San Francisco$150k–$220kfulltimemidAdded today

About this role

Join an early-stage healthtech marketplace as an AI Engineer to build and maintain patient-facing AI conversation systems. You'll diagnose production failures, develop agent infrastructure, and ship fixes that directly improve patient outcomes in a hands-on, end-to-end ownership role.

What you'll do

  • Diagnose and fix failures in patient-facing AI conversations in production
  • Build and maintain agent infrastructure including tool integrations, evaluation frameworks, and retrieval systems
  • Automate clinic operations workflows to eliminate manual toil
  • Handle patient interactions with empathy when AI systems fall short and extract product insights
  • Own systems end-to-end with minimal oversight, making independent architectural decisions

What they're looking for

  • LLM and agent systems development
  • Python or equivalent programming language
  • Agent infrastructure (evals, RAG, retrieval systems, tool integrations)
  • Computer science and mathematics fundamentals
  • Production debugging and code shipping
  • Vector databases and embedding systems (preferred)
  • Prompt engineering and conversational AI (preferred)
  • Automation and workflow optimization

Benefits

  • Meaningful work directly improving patient healthcare access
  • End-to-end ownership of critical systems
  • Fast-moving startup environment with learning opportunities
  • Competitive salary aligned with AI engineering market rates
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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 an LLM or agent system you built end-to-end—what was your architecture and how did you evaluate success?
  • Describe a time you diagnosed a production failure in an AI system. What was your debugging process and how did you prevent recurrence?
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