Senior Researcher, MIT AI Risk Initiative

Position overview

As AI capabilities rapidly advance, we face critical information gaps in effective AI risk management:

• What are the risks from AI, which are most important, and what are the critical gaps in response?
• What are the mitigations for AI risks, and which are the highest priority to implement?
• Which AI risks and mitigations are relevant to which actors and sectors?
• Which mitigations are being implemented, and which are neglected?

We are hiring a Research Scientist to strengthen the MIT AI Risk Initiative’s applied research, stakeholder engagement, and project delivery.

This is a research staff role. The primary function is to lead and deliver applied research work streams that help decision-makers understand AI risks, mitigations, organizational responses, and gaps in practice. The role also includes substantial responsibility for stakeholder engagement, partnership management, and project operations, because our research is designed to be timely, decision-relevant, and useful to policymakers, industry, civil society, and researchers.

The ideal candidate will be comfortable moving between rigorous applied research, practical project delivery, and external engagement. They should be able to design and execute evidence synthesis and survey work, translate findings into useful outputs, manage contributors and workflows, and engage with collaborators, funders, and end users to ensure the work addresses real decision needs.

• Evidence synthesis and measurement
  • Design and execute systematic reviews of organizational AI risk responses (search, screening, extraction, coding, and quality assurance).
  • Develop and maintain research protocols, codebooks, and documentation to ensure results are reproducible and updateable over time.
  • Analyze qualitative and quantitative data and synthesize findings into clear conclusions.
• Surveys and expert input
  • Design and field surveys to gather structured input from relevant populations (for example, experts, practitioners, or organizations).
  • Analyze results and integrate them with evidence from literature and documents.
• Research outputs and decision support
  • Write and disseminate research outputs for both technical and applied audiences (datasets, memos, briefs, and publications).
  • Translate findings into practical decision-support tools for end users (for example, structured datasets, frameworks, and guidance materials).
• Engage stakeholders across government, industry, civil society, and research to understand decision needs and ensure outputs are usable.
• Support external meetings, briefings, and workshops; communicate results clearly to non-specialist audiences.
• Help manage relationships with collaborators, funders, and end users, including responding to inquiries and coordinating inputs.
• Represent the project in selected external meetings, briefings, workshops, and events, in coordination with the broader team.
• Project delivery and operations
  • Plan and deliver work streams end-to-end, including scoping, timelines, resourcing, and risk management.
  • Manage project coordination logistics and maintain clear process documentation.
  • Track budgets and contractor spend where relevant; support procurement and payments in coordination with MIT processes.
• Grants and funding support
  • Contribute to grant and proposal development (narrative sections, workplans, budgets, and supporting materials).
  • Support funder updates and reporting by translating progress into clear milestones, outputs, and next steps.
• Lab participation
  • Participate actively in the MIT Future Tech research community by attending lab meetings, sharing updates on work streams, and contributing feedback on related projects.
  • Collaborate with other lab members to align methods, improve research quality, and identify new…