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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded today

About this role

Mid-level AI Engineer at a pre-seed HR tech startup in San Francisco, building agentic systems and RAG pipelines for enterprise talent matching. You'll own production AI systems end-to-end, from design through deployment, collaborating closely with founders and product in a fast-moving, technical environment.

What you'll do

  • Design and maintain agentic systems automating multi-step workflows across regulated domains
  • Own production RAG pipelines, vector databases, embeddings, and retrieval infrastructure
  • Implement multi-agent orchestration, tool-calling, memory, and reasoning components
  • Develop evaluation and safety infrastructure to measure performance and enforce reliability
  • Ship full-stack AI products from MVP to enterprise-grade with APIs, data models, and CI/CD
  • Collaborate cross-functionally to prioritize work and iterate based on user feedback

What they're looking for

  • LLM deployment and production optimization
  • Python and TypeScript/React
  • RAG patterns and vector databases
  • AWS or GCP cloud platforms
  • API design and high-throughput system architecture
  • Automated testing and AI system evaluation
  • Relational and NoSQL databases
  • Agent frameworks (LangGraph, CrewAI) or orchestration tools

Benefits

  • Salary $180,000 – $400,000 USD annually
  • Early-stage equity opportunity
  • High-ownership role with significant impact
  • Lean, experienced core team
  • Work directly with founders and product leadership
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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 system you've built—how did you handle prompt engineering, orchestration, and monitoring for reliability?
  • Describe your experience designing and implementing a RAG pipeline. What vector database did you use, and how did you optimize retrieval quality?
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