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Clera

AI Engineer

San Francisco$150k–$200kfulltimemidAdded today

About this role

Join a healthtech startup as an AI Engineer to build and maintain patient-facing conversational AI systems for medical tourism. You'll own the full stack of LLM agent infrastructure, diagnose production failures, and balance technical excellence with patient empathy in a high-impact role.

What you'll do

  • Debug and fix AI conversation failures with patients, shipping code solutions to prevent recurrence
  • Build and maintain agent infrastructure including tool integrations, evaluation frameworks, and retrieval systems
  • Automate recurring manual workflows on the clinic side to reduce operational toil
  • Provide direct patient support with empathy when AI systems fall short, converting those insights into product improvements
  • Own systems end-to-end with minimal oversight, making independent architectural decisions

What they're looking for

  • LLM and agent systems (design, building, and shipping)
  • Python or equivalent backend language
  • Agent infrastructure (evals, RAG/retrieval, tool integrations)
  • Production debugging and diagnostics for LLM systems
  • Vector databases and embedding systems
  • Prompt engineering and conversational AI evaluation
  • Startup autonomy and independent system ownership
  • Patient communication and empathy

Benefits

  • Base salary $150,000–$200,000 annually
  • Fully on-site role in San Francisco with no remote flexibility
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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 time you diagnosed and fixed a failure in a production LLM or agent system. What was your debugging process?
  • Walk us through how you've built a RAG system or retrieval-based agent from scratch. What challenges did you encounter?
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