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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded today

About this role

Join a lean, fast-moving AI team in San Francisco to build production LLM-based systems that automate complex workflows in regulated industries. You'll own the full stack—from agentic systems and RAG pipelines to evaluation infrastructure—and ship user-facing AI products with direct founder collaboration.

What you'll do

  • Design and build agentic systems automating multi-step workflows in healthcare, legal, fintech, logistics, and compliance
  • Own production RAG pipelines, vector databases, embeddings, and retrieval infrastructure at scale
  • Implement multi-agent orchestration, tool-calling, memory, and reasoning components
  • Develop evaluation and safety infrastructure to measure performance and enforce enterprise reliability
  • Ship full-stack AI products: APIs, data models, frontend/backend, CI/CD, monitoring, and testing
  • Collaborate with founders, product, and design to prioritize work and iterate on user feedback

What they're looking for

  • Production LLM deployment and orchestration
  • Python and TypeScript/React
  • RAG patterns, vector databases, and embeddings
  • AWS or GCP cloud infrastructure
  • Relational and NoSQL databases
  • API design and high-throughput systems
  • Automated testing and AI system evaluation
  • Multi-agent frameworks (e.g., LangGraph, CrewAI)

Benefits

  • Early-stage equity in pre-seed company with strong investor backing
  • High ownership on a lean team with direct product impact
  • Full-stack technical autonomy across AI infrastructure
  • Opportunity to work on regulated, enterprise-grade domains
  • Close collaboration with founders and early-stage learning
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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 deployed—what were the biggest orchestration or reliability challenges?
  • How have you approached evaluating and monitoring AI systems in production beyond initial testing?
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