Software Engineer, Automation

Mercor's mission is to organize human intelligence to power the AI economy. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

As a Software Engineer on the Automations 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.

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  Build and maintain MCP servers that give agents structured access to Mercor’s platform reading project state, taking management actions, and triggering communications.

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  Design and implement evaluation frameworks to measure agent decision quality and drive continuous improvement through feedback loops.

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  Develop action queues and playbook orchestration systems that allow agents to execute multi-step workflows with conditional logic.

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  Instrument agent systems end-to-end — capturing outcomes, closing feedback loops, and enabling hill-climbing on eval metrics.

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  Collaborate directly with operators to identify high-leverage automation opportunities and translate them into robust agent behaviors.

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  Debug and harden agent systems in production, designing for reliability at scale.

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  Contribute to foundational patterns and infrastructure that the rest of the company will build on.

• 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.

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

• Impact: Your work powers how the world’s leading AI labs train and test their models.

• Learning: Get early insights into frontier model capabilities months before the market.

• Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership.