Clera
Applied AI Engineer
About this role
Join a well-funded AI infrastructure startup as a founding engineer to build core systems enabling AI agents to execute reliably, maintain long-term context, and handle human oversight in production environments. You'll own the full lifecycle from infrastructure design through customer deployment, directly shaping how agents operate at scale.
What you'll do
- Build and maintain core infrastructure for reliable agent execution across extended iterations
- Design memory systems that retain months of client context from real-world operational data
- Develop evaluation harnesses trusted for production deployment decisions
- Own full loop of building, measuring, breaking, fixing, and improving agent systems based on feedback
- Ship agent systems directly to customers and iterate based on live usage
- Contribute to infrastructure, orchestration, customer collaboration, and early hiring as needed
What they're looking for
- Production LLM agent development and deployment
- Python for production systems
- Agent memory management and context retrieval
- Agent orchestration frameworks and workflow management
- Evaluation framework design and implementation
- Failure recovery and reliability patterns
- RAG and retrieval systems on operational data
- Human-in-the-loop workflow implementation
Benefits
- Meaningful early-stage equity stake
- Relocation support provided
- Visa sponsorship available
- Founding engineer influence on product direction
- Work directly with customers on production systems
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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.
View all jobs at CleraLikely interview questions
- Walk us through a production LLM agent system you shipped—how did you handle reliability and what broke in production?
- Describe an eval harness you built that actually influenced a production deployment decision. What made it trustworthy?