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Mercor is an AI data company on a mission to organize human intelligence to power the AI economy. The company operates a platform where millions of domain experts train frontier AI models, earning over $4 million per day collectively. Mercor is profitable, Series C funded, valued at $10 billion, and works in-person five days a week from San Francisco, NYC, or London offices.
As a Software Engineer on the backend team, you'll join a small, product-driven engineering group building modern systems that power critical workflows for leading AI companies. You'll own projects end-to-end—designing, building, and shipping production systems that directly impact customers. From day one, you'll write production-grade code, reason about systems holistically, and make decisions tied to business outcomes. You'll collaborate closely with product and design teams, moving fast while maintaining high standards for code quality, scalability, and simplicity.
You'll have the opportunity to be team-matched to where you can have the most impact: either the talent platform side (building next-generation recruiting automation, intelligent candidate matching, and global hiring infrastructure with seamless contracts and payments) or the applied AI/human data side (partnering with top AI researchers from OpenAI, Anthropic, and Google to create post-training datasets that improve foundational model capabilities).
Key responsibilities include designing and building scalable backend APIs, services, and infrastructure; collaborating across the stack with product and design to ship features quickly; writing clean, maintainable code and contributing to documentation; participating in code reviews, technical design discussions, and retrospectives; solving real-world customer problems under real-world constraints; and contributing to core systems that power training, evaluation, and scaling for leading AI labs.
You should have strong coding fundamentals in at least one modern language (Python, Go, Rust, etc.), understanding of data structures, algorithms, and distributed systems, familiarity with APIs, SQL/NoSQL databases, and cloud platforms (AWS/GCP), a bias toward ownership and curiosity, and the ability to break down ambiguity and ship simple solutions to complex problems.
The role offers significant impact—your work powers how the world's leading AI labs train and test their models. You'll gain early insights into frontier model capabilities months before market release and have fast paths to ownership working on both infrastructure and research-adjacent projects.
