Role overview

We are looking for engineers who build and operate LLM agents in production, and who have real visibility into how agents are actually used inside a company.

You have probably:

- Shipped an agent that real users depended on, and been on the hook when it broke.

- Figured out how to tell whether an agent got better or worse after a change.

- Run agents that outlive a single request: scheduled jobs, long-running work, cloud sandboxes.

- Watched your org build an internal assistant, and seen who adopted it and who quietly did not.

We are especially interested in the layers most people do not talk about: internal monoagents wired into company data, shared company memory, reusable skills and playbooks, the tool and MCP surfaces agents call, and how anyone sees what agents did and what they cost.

Applying starts with a short conversational AI interview. No coding, no take-home. We want to hear how you actually think about agent reliability, evaluation, and adoption, and the tradeoffs you have made in real systems. Bring war stories. The messier and more specific, the better.

If that screen stands out, we will invite you to a live 30 minute conversation with our team. We pay $100 to $500 for that conversation, paid on completion of the call, with the amount depending on depth of experience.

If that sounds like you, apply and complete the screen. We review every submission.

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