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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded 1 month ago

About this role

Join a pre-seed AI startup building agentic systems that automate complex workflows in regulated industries. As a mid-level AI Engineer, you'll own production LLM services end-to-end, design RAG pipelines and multi-agent orchestration, and ship full-stack AI products with real enterprise impact.

What you'll do

  • Design and maintain agentic systems automating multi-step workflows in 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 for model performance and reliability
  • Ship full-stack AI products from MVP to enterprise-grade with APIs, frontend/backend, and production operations
  • Collaborate with founders and product to define success metrics and iterate based on user feedback

What they're looking for

  • LLM deployment and production optimization
  • Python, TypeScript/React, or equivalent full-stack languages
  • RAG patterns, vector databases, embeddings, and retrieval systems
  • Cloud platforms (AWS or GCP)
  • Multi-agent orchestration and agentic frameworks
  • Testing, evaluation, and monitoring for AI systems
  • API design and high-throughput system architecture
  • Relational and NoSQL databases

Benefits

  • Competitive salary: $180,000–$400,000 USD annually
  • Early-stage equity stake
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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. How did you handle prompt engineering, model selection, and what was your approach to detecting and preventing failures?
  • Describe your experience building RAG pipelines. What vector database did you use, how did you handle embeddings and indexing, and what trade-offs did you consider?
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