The posting, in Mercor's own words
archived Oct 28, 2026About Mercor
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. 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.
About the Role
We’re looking for an Analytics Engineer to build the data infrastructure, analytics, and automations that power decision-making and operations across Mercor. This is a high-ownership role where you’ll work directly with business leaders to identify problems, define roadmaps, and deliver solutions end-to-end. Rather than waiting for stakeholder requests, you’ll proactively uncover bottlenecks and opportunities, often before others recognize them. We’re looking for someone with exceptional technical ability, strong business judgment, and a bias for action. You should be a fast learner who leverages AI tools to dramatically increase your productivity and thrives in a high-intensity environment.
Read the full posting ↓
What You’ll Do
Drive your own roadmap: Partner directly with stakeholders, proactively identify high-impact problems, and independently prioritize and execute solutions. Build scalable data infrastructure: Develop reliable data pipelines, models, and metrics using SQL, dbt, and Snowflake. Own analytics end-to-end: Translate ambiguous business problems into production-ready data models, dashboards, and analytical tools. Automate business workflows: Identify operational bottlenecks and build automations that eliminate manual work and improve efficiency. Leverage AI to multiply output: Use AI coding agents and emerging tools to accelerate development and continuously improve how analytics engineering gets done.