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Engineering Manager, Fraud & Compliance

Mercor

San FranciscoOn-siteFullTime

About this role

About 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. We're building the infrastructure that powers one of the world's largest expert networks and delivers expert data at unprecedented scale. We're looking for exceptional engineering leaders to help us protect the integrity of that ecosystem — building the fraud, risk, and compliance systems that make a global expert marketplace trustworthy at scale. We value builders who have repeatedly chosen difficult problems, thrived in environments with high expectations, and can point to concrete examples where their leadership materially changed the trajectory of a team, product, or company. Why This Role We're still early. Fraud is one of Mercor's most important business challenges. Every day, we process enormous volumes of applications, assessments, identity verifications, and work activity across a global talent network. Protecting that ecosystem requires sophisticated systems that can identify fraudulent behavior, evaluate risk, and make accurate decisions at scale — and the playbooks for do

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