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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded today

About this role

Mid-level AI engineer to build agentic systems automating multi-step workflows at a pre-seed startup. You'll own full-stack AI products from design to deployment, working across RAG pipelines, multi-agent orchestration, and enterprise safety infrastructure in regulated domains.

What you'll do

  • Design and build agentic systems that automate complex workflows in healthcare, legal, fintech, and compliance domains
  • 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 from MVP to production, including APIs, data models, CI/CD, and monitoring
  • Collaborate with founders and product teams to prioritize work and iterate based on user feedback

What they're looking for

  • Python and TypeScript/React development
  • Production LLM deployment, prompt design, and tool integration
  • RAG patterns, vector databases, embeddings, and retrieval pipelines
  • AWS or GCP cloud platforms
  • Relational and NoSQL databases
  • AI system testing, evaluation, and monitoring
  • API design and high-throughput systems
  • Agent frameworks (LangGraph, CrewAI) and workflow orchestration (Temporal, Trigger)

Benefits

  • Equity in pre-seed startup
  • Work directly with founders on impactful AI products
  • Full-stack ownership from conception to deployment
  • Exposure to regulated industries and enterprise-grade systems
  • Opportunity to shape core product strategy in early-stage company
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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 was the architecture, and how did you handle prompt reliability or tool integration?
  • Describe your experience with RAG pipelines in production. What retrieval patterns did you use, and how did you optimize for accuracy and latency?
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