Software Engineer, Automation

Mercor•San Francisco, CA

2d•Onsite

About The Position

As a Software Engineer on the Agents team at Mercor, you’ll join a small, high-leverage engineering group building the autonomous systems that run Mercor’s internal operations — and eventually, operations at scale for the companies we serve. This is not a research role. You’ll design and ship production agents that take real actions: scheduling interventions, executing operational playbooks, managing talent pipelines, and surfacing decisions that previously required hours of manual click-ops. From day one, you’ll own systems end-to-end — from MCP server design to evaluation frameworks — working directly with operators and platform engineers to turn repeatable human workflows into reliable, autonomous ones. You’ll reason about correctness, rollback, observability, and trust in systems that touch payments, contracts, and people’s livelihoods. This is greenfield agentic infrastructure with immediate real-world impact.

Requirements

Strong backend engineering fundamentals in Python (or equivalent modern language).
Experience building with LLM APIs — including tool use, structured outputs, and multi-step reasoning workflows.
Comfort designing evaluation frameworks and thinking rigorously about agent failure modes.
Ability to ship quickly and iterate on systems that interact with the real world.
Understanding of distributed systems, APIs, and production infrastructure.

Nice To Haves

Full-stack experience is a plus — owning features end-to-end will increase your impact.

Responsibilities

Build and maintain MCP servers that give agents structured access to Mercor’s platform reading project state, taking management actions, and triggering communications.
Design and implement evaluation frameworks to measure agent decision quality and drive continuous improvement through feedback loops.
Develop action queues and playbook orchestration systems that allow agents to execute multi-step workflows with conditional logic.
Instrument agent systems end-to-end — capturing outcomes, closing feedback loops, and enabling hill-climbing on eval metrics.
Collaborate directly with operators to identify high-leverage automation opportunities and translate them into robust agent behaviors.
Debug and harden agent systems in production, designing for reliability at scale.
Contribute to foundational patterns and infrastructure that the rest of the company will build on.