Mercor AI Safety Fund Grants

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.

Mercor Safety Research Grants $5M

One of the biggest challenges the industry faces today is addressing whether frontier AI is safe enough to deploy. A model can pass safety checks and still act differently in production. This is why investing today in safety research, evals and verification is critical.

Mercor is committing $5 million to fund safety research. The grant supports:

• Researcher hours

• API credits

• Stipends for event and conference attendance

• The time of experts from Mercor's platform

This is separate from the Mercor Research Fellowship, which funds benchmark and economics work. You can apply to that HERE.

What we're looking for

We're open to proposals across the full range of safety interests. We're particularly interested in:

• Misalignment: deceptive alignment, goal misgeneralization, reward hacking, scheming, and situationally-aware failure modes

• Sandbox escape: containment failures, privilege escalation, tool misuse, and agents operating outside their intended scope

• Evaluation awareness: models detecting they are being tested and behaving differently under observation than in deployment

• Interpretability: understanding what models are actually doing internally, and whether that can be made legible to a human reviewer

• Oversight and control: scalable supervision, human-in-the-loop reliability, and what breaks when the system is more capable than its reviewer

• Red-teaming methodology: more robust systems for uncovering novel failures

If your work doesn't fit neatly into these, apply anyway. Strong proposals outside this list are welcome.

Why us

• Funding for researcher time, API credits, and event attendance

• Where useful to the work: access to Mercor's expert network for human grading, red-teaming, and annotation: lawyers, accountants, engineers, scientists, clinicians

• Access to Mercor's internal evaluation infrastructure, subject to review

• Introductions to Mercor's network of researchers across frontier labs and academia

Who should apply

• Independent researchers, academics, PhD students, and small teams

• People with a specific, well-scoped question: the grant is built around your proposal, not a generic research rotation

• Background in ML, CS, statistics, or an adjacent field (measurement, psychometrics, HCI, security, social science)

• Bonus: experience with agentic evaluation, RL environments, adversarial ML, or systems security

We expect grantees to publish. A paper, an open dataset, a public methodology, or a tool the field can use.

How to apply

Submit an Expression of Interest. We expect to see a one- or two-page document. It should contain at least a section on your team, background, and research accomplishments; a section on your proposed research project; and a section on the outputs and impact of the project, with directionally correct timelines and resource requirements.