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Mercor in San Francisco seeks engineers who build and operate LLM agents in production, with real visibility into how agents are used inside a company.
You have shipped an agent used by real users, and can tell if a change improves or degrades performance; you design for long-running workflows, scheduled jobs, and cost-aware operation across data sources.
You will work on internal surfaces that expose agent behavior and cost, with a focus on reliability, observability, and scalable tooling.
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.