New role: Senior Researcher - MIT AI Risk Initiative

By Peter Slattery 🔸 @ 2026-07-30T19:34 (+15)

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?
• How is the above changing over time?

Within MIT FutureTech, the MIT AI Risk Initiative aims to provide credible, timely, and decision-relevant answers to these questions. Our core outputs include the risk repository, incident tracker, mitigations database, and governance map.

We are hiring a Senior Researcher 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 workstreams 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.

What you’ll do

• 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).
• Stakeholder engagement
   
  • 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 workstreams 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 FutureTech research community by attending lab meetings, sharing updates on workstreams, and contributing feedback on related projects.
  • Collaborate with other lab members to align methods, improve research quality, and identify new research opportunities.
• Team leadership
   
  • Manage and mentor junior researchers.
  • Coordinate work with internal and external contributors (including contractors where relevant).

Supervision Received

• Reports to Neil Thompson, with day-to-day supervision and project guidance from Peter Slattery and other members of the team as appropriate.
• Works under general oversight with direction on non-routine issues  

Supervision Exercised

• May guide the work of internal and external project support staff and writers
• May provide coaching and on-the-job training 

Qualifications & Skills

Minimum Required Education and Experience

• 5+ years experience in applied research methods
• Publications or research output in an applied social science (e.g., economics, psychology, behavioral science) or relevant field
• Demonstrated ability in conducting systematic reviews and surveys.
• In-depth understanding of principles and practice of research
• Prior experience in consulting, project management, or operations, preferably in a research, academic, or technology-oriented environment
• Strong analytical skills with both qualitative and quantitative data
• Stakeholder engagement experience, such as working with clients, partners, funders, or end users to understand needs and communicate results clearly.
• Leadership experience, including supervising, mentoring, or coordinating junior researchers and collaborators and leading research projects, programs, or functions.
• Operational competence in a research, academic, consulting, or technology-oriented environment (for example, maintaining process documentation, coordinating vendors/contractors, and navigating administrative workflows).
• Comfort with budgets and resourcing, such as tracking spend against a plan, managing contractor time, or supporting financial reporting (depth can vary; we are looking for practical fluency).

Preferred Education 

• PhD degree
• Honours degree or higher in an applied social science (e.g., psychology, behavioral science) or relevant field

Preferred Experience

• Grant writing experience
• Consulting experience
• Experience producing decision-focused outputs (for example, policy briefs, executive memos, toolkits, or structured evidence summaries).
• AI risk, AI governance, or AI safety expertise

Other information

• One-year term based on research grant funding, with possibility of renewal subject to funding and project needs.
• This is an on-site role based at MIT in Cambridge, MA.
• We have a slight preference for candidates who can work hours that overlap with Australia (later afternoon and evening).
• Full-time is preferred, but part-time commitments will also be considered.
• We can provide a J-1 work visa for applicants with PhDs

Selection process

• Short test task
• Interview
• Potential paid work trial

About MIT FutureTech 

MIT FutureTech is an interdisciplinary group of  economists, computer scientists, and engineerswho study the foundations and economic implications of progress in computing and Artificial Intelligence.  Economic and social change is underpinned by advances in computing: for instance, improvements in the miniaturization of integrated circuits, the discovery and refinement of algorithms, and the development and diffusion of better software systems and processes. We aim to identify and understand the trends in computing that create opportunities or risks and help leaders in computing, scientific funding bodies, and government to respond appropriately. 

Our research therefore helps to answer important questions including: Will AI progress accelerate or decline – and should it? What are the implications for economic growth and for the labor markets? What are the bottlenecks to growth from AI, and how can they be solved? What are the risks from AI, and how can we mitigate them? 

To support our research, we run seminars and conferences to better connect the field of computer scientists, economists, and innovation scholars to build a thriving global research community. 

To disseminate it, we advise governments, nonprofits and industry, including via National Academies panels on transformational technologies and scientific reliability, the Council on Competitiveness’ National Commission on Innovation and Competitiveness Frontiers, and the National Science Foundation’s National Network for Critical Technology Assessment. 

Our work has been funded by Open Philanthropy, the National Science Foundation, Microsoft, Accenture, IBM, the MIT-Air Force AI accelerator, and the MIT Lincoln Laboratory. 

Some of our recent outputs:  

• Prioritization of Risks from Artificial Intelligence June 2026 A Delphi Study of 272 International Experts
• Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks
• ‍The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence
• Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?
• How industry is dominating AI research   
• The Quantum Tortoise and the Classical Hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not)
• A workshop on AI scaling and its implications for AI development, automation, and more
• The Great Inflection? A Debate About AI and Explosive Growth   
• There’s plenty of room at the Top: What will drive computer performance after Moore’s law?   
• Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable
• America’s lead in advanced computing is almost gone
• The Decline of Computers as a General Purpose Technology: Why Deep Learning and the End of Moore’s Law are Fragmenting Computing
• How Fast Do Algorithms Improve?  

Some recent articles about our research: 

• Time: What the Numbers Show About AI's Harms
• Techcrunch: MIT researchers release a repository of AI risks
• CNN: AI and the labor market: MIT study findings
• TIME: AI job replacement fears and the MIT study
• Boston Globe: AI's impact on jobs according to MIT 

You will be working with Dr. Neil Thompson, the Director of MIT FutureTech. Prior to starting FutureTech, Dr. Thompson was a professor of Innovation and Strategy at the MIT Sloan School of Management.  His PhD is in Business & Public Policy from Berkeley.  He also holds Master’s degrees in: Computer Science (Berkeley), Economics (London School of Economics), and Statistics (Berkeley).  Prior to joining academia, Dr. Thompson was a management consultant with Bain & Company, and worked for the Canadian Government and the United Nations.

‍About the MIT Computer Science and Artificial Intelligence Lab (CSAIL) 

CSAIL is one of the world’s top research centers for computer science and artificial intelligence (currently ranked #1).  It has hosted 9 Turing awards winners (the “Nobel Prize of Computing”) and has pioneered many of the technologies that underpin computing.

How to apply

Please use this form to register interest in this role or to submit a general expression of interest.

Selected candidates will be first interviewed via Zoom. We are recruiting on a rolling basis and may close applications early if we find a suitable candidate, so please apply as soon as possible to maximize your chances.

** To comply with regulations by the American with Disabilities Act (ADA), the principal duties in position descriptions must be essential to the job. To identify essential functions, focus on the purpose and the result of the duties rather than the manner in which they are performed. The following definition applies: a job function is essential if removal of that function would fundamentally change the job.

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