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

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.