Mercor AI Research Fund Grants: Benchmarks

About this role

ABOUT MERCOR Mercor's mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise. We built our training and evaluation infrastructure alongside the world's leading AI labs. Now, we're bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows. Our work sits at the intersection of frontier AI, human expertise, and real-world application. We're solving problems few companies have encountered, with the opportunity to influence what the world's most advanced AI systems become capable of doing. We're looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You'll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence. Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London. MERCOR AI RESEARCH FUND GRANTS: BENCHMARKS One of the biggest challenges facing the industry today is determining whether frontier AI is capable enough to deliver value in real-world settings. A model can perform well on benchmarks and still struggle in production. That's why investing in research, realistic evaluations, and robust verification is critical. Mercor is seeking to fund AI research on benchmarking. This is separate from the Mercor Research Fellowship, which funds individual experts (apply HERE https://www.mercor.com/careers/a0a98be0-d856-4129-b500-c0a3e412ef01/), our $5m of safety funding awards (apply HERE https://jobs.ashbyhq.com/mercor/1d59ce50-4207-4d95-b7fd-5a01e90b0897), and our research funding on the economic impacts of AI. The grant supports: - The time of experts from Mercor's platform - Researcher hours - API costs - Stipends for event and conference attendance BUDGET CATEGORIES Applications are grouped into three budget tiers. Please state your tier clearly in your application and ensure your overall budget ask falls within it: - Tier 1: under $50,000: Suited to focused, well-scoped projects such as pilot studies, evaluations, replications, or extensions of existing work. These applications undergo a streamlined review. - Tier 2: under $200,000: Suited to substantial projects requiring dedicated personnel time, meaningful compute, or data collection. Reviewed with proportionally greater attention to the budget breakdown and implementation plan. - Tier 3: under $500,000: Reserved for ambitious, multi-component research programs. These applications receive the highest level of scrutiny: reviewers will expect a detailed justification of each spend category, a clear account of team capacity to execute at this scale, and milestones against which progress can be assessed. The level of review scrutiny scales with the size of the ask, so applicants should request the smallest budget that credibly supports the proposed work. RESEARCH AGENDA We are interested in submissions in the following area: - Real-world evaluations: measuring whether benchmark performance translates into reliable performance on realistic tasks and workflows - Environments: building high-fidelity, interactive environments that capture the complexity of real-world work - Long-horizon tasks: evaluating and improving models' ability to plan, execute, and adapt across extended workflows - Agentic capabilities: tool use, coordination, memory, and autonomous execution - Reasoning and problem-solving: improving performance on complex, ambiguous, or underspecified tasks - Post-training: developing better data, rewards, and training methods to improve model capabilities - Evaluation methodology: creating more robust measures of capability, reliability, and real-world utility ELIGIBILITY - Researchers at universities and non-profit research organizations, as well as public benefit corporations and small startups ( 20 people) that do not sell AI training data. Awards are made to the researcher's institution. - Applicants must be based in the United States or the United Kingdom We expect grantees to publish a paper, an open dataset, a public methodology, or a tool the field can use, released under an open license that permits commercial use. TIMELINE - Application deadline: November 27, 2026, 11:59 PM PT (UTC−8) - Notification to applicants: by December 18, 2026 Late or incomplete applications will not be reviewed. All applicants will be notified of the outcome whether or not they are shortlisted. HOW TO APPLY We invite interested researchers to submit a brief Expression of Interest (EOI) before preparing a full proposal. The EOI should be ~1-3 pages and include: - Research question and background (max 250 words): What are you trying to do? State your objective in plain language, with minimal jargon. How is this problem addressed today, and what are the limits of current practice? Who cares: if you succeed, what difference will it make, and to whom? - Project proposal (max 400 words): Concrete technical and implementation detail: your approach, methods, datasets, models, compute, and evaluation plan. - Novelty (max 200 words): What is new in your approach relative to existing and ongoing work, and why do you believe it will succeed where current practice falls short? - Risks (max 150 words): What are the main technical and execution risks, and how will you mitigate or detect t

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