‍Position Overview 

MIT FutureTech is developing LLM-augmented pipelines to accelerate evidence synthesis and systematic reviews for AI risks and mitigations, as part of the MIT AI Risk Initiative. 

The immediate use case is to help build out modules to support our review of global organizations’ AI risk responses, where we identify public documents, screen for relevance, extract claims about AI risks/mitigations, and classify outputs against several taxonomies. 

The bigger picture includes generalizing and adapting this pipeline to support living updates & extensions for our risk repository, incident tracker, mitigations review, and governance mapping work. 

By contributing your skills to the MIT AI Risk Initiative, you’ll help us provide the authoritative data and frameworks that enable decision-makers across the AI ecosystem to understand & address AI risks.

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What you’ll do:

• Phase 1: Org review pipeline (Jan–Mar)
  
  • Build/improve modules for document identification, screening, extraction, and classification
  • Build/improve human validation / holdout sampling processes and interfaces so we can measure performance against humans at each step
  • Integrate modules into an end-to-end evidence synthesis pipeline
  • Ship something that helps us complete the org review by ~March
• Phase 2: Generalization & learning (Mar onwards)
  
  • Refactor for reuse across different AI Risk Initiative projects (incidents, mitigations, governance mapping)
  • Implement adaptive example retrieval 
  • Build change tracking: when prompts or criteria change, what shifts in outputs?
  • Help us understand where LLM judgments can exceed human performance and thus be fully automated, and what still needs human review (and design interfaces / processes to enable this)
  • Document architecture and findings for handoff

Required skills

• Strong software engineering fundamentals 
• Hands-on experience building LLM pipelines
• Python proficiency
• Comfort working on ambiguous problems where "what should we build?" is part of the work
• Can communicate clearly with researchers who aren't software engineers
  

Nice to have

• Prior work in research, systematic review, or annotation/labeling contexts
• Experience with evaluation/QA/human validation
• Familiarity with embeddings + vector search for example retrieval
• API integrations (Airtable or similar), Extract, Transform, Load (ETL)/scraping-adjacent work
  

Selection process

• Short technical interview + review of relevant prior work
• Potential paid work trial 

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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:  

• 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: 

• 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.

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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.

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How to apply

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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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