Mercor Research Fellowship — APEX

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. About the Fellowship Mercor’s APEX benchmark family measures whether frontier AI models can actually do economically valuable work: multi-hour agentic tasks in investment banking and corporate law, real professional accounting workflows, real-world software engineering, and graduate-level science. Every APEX benchmark is built and validated with Mercor’s network of domain experts — not written from a textbook. Alongside APEX, Mercor's economics team works to understand how AI is changing the labor market and tries to quantify its economic impacts. To do so, we leverage Mercor’s unique access to data, including: a marketplace with millions of candidates, internal AI usage metrics, enterprise data, and more. The Mercor Research Fellowship funds people to build the next generation of benchmarks, evaluation techniques, and economic analyses of AI. You can pitch a benchmark or eval methodology you want to build — a new domain, a harder task format, a better way to measure agentic reliability — and if selected, you get the time, compute, expert labor, and mentorship to design, implement, and release it end to end. Or, on the economics side, you can pitch a study using Mercor’s proprietary data to better understand an aspect of AI’s impact on the economy and firms. You’ll work directly with the APEX research team, get access to real enterprise evaluation problems from Mercor’s Fortune 500 and frontier-lab partners, and see your work shape how the industry measures AI capability. Program Details Duration: 3–6 months, rolling admission Commitment: minimum 30 hours/week; full-time preferred Location: remote, or in-person at Mercor’s San Francisco office Admission: apply with a specific benchmark or eval technique you want to build — the fellowship is funded around your pitch, not a generic research rotation What You’ll Do Propose and scope a new benchmark evaluation technique, or economic study in an area not covered well by existing work Build and validate it. For benchmarks, that means designing task specifications and grading rubrics with Mercor's network of vetted experts — lawyers, accountants, engineers, scientists, consultants — then piloting tasks, calibrating scoring, and running frontier models to analyze where and why they fail. For economic studies, this can mean anything from designing and running field experiments with experts on our platform to conducting empirical analyses of enterprise data. Publish your results : a paper, an open dataset, a new leaderboard on APEX, or a methodology the team adopts internally — and partner with Mercor's research and engineering teams to fold what you learn back into our public work. Focus Areas Long-horizon, multi-app agentic tasks in professional services (law, finance, consulting) — extending APEX-Agents Real-world software engineering evaluation beyond issue resolution — extending APEX-SWE Professional accounting and finance workflows — extending APEX-Accounting AI-for-Science evals: research-level mathematics, biology, materials science, and theoretical physics New economic benchmarks — negotiation, management, and other capabilities that carry economic value but resist standard task formats Novel evaluation methodology: contamination resistance, rubric design, human-vs-model grading agreement, cost-adjusted scoring The economics of human data platforms like Mercor – pricing, matching, elasticity of supply and demand. AI Theory of the Firm: what an AI-native firm looks like from the inside,