Application Guide

How to Apply for ML Engineer, AI Risk Initiative

at Massachusetts Institute of Technology, FutureTech

🏢 About Massachusetts Institute of Technology, FutureTech

FutureTech at MIT is an interdisciplinary research group focused on the economic and technical foundations of computing progress, operating at the intersection of academia and real-world impact. This role offers the unique opportunity to contribute to MIT's prestigious AI Risk Initiative while working remotely, combining academic rigor with practical engineering to address critical AI safety challenges.

About This Role

This part-time ML Engineer role involves building LLM-augmented pipelines specifically for evidence synthesis on AI risks and mitigations, requiring both technical development and human-AI collaboration design. You'll be creating reusable systems for systematic review processes that directly support MIT's AI Risk Initiative projects, making tangible contributions to AI safety research through practical engineering solutions.

💡 A Day in the Life

A typical day involves developing and testing LLM modules for document classification, integrating human feedback loops into automated screening pipelines, and refactoring code components for reuse across different AI risk assessment projects. You'll collaborate with researchers to optimize evidence synthesis workflows while documenting systems for knowledge transfer within the initiative.

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🎯 Who Massachusetts Institute of Technology, FutureTech Is Looking For

  • Has hands-on experience building LLM-augmented pipelines for document processing, not just theoretical ML knowledge
  • Can demonstrate practical experience with evidence synthesis or systematic review workflows in previous projects
  • Shows ability to design systems that effectively integrate human validation with automated processes
  • Has experience refactoring codebases for reuse across multiple projects, not just single-use implementations

📝 Tips for Applying to Massachusetts Institute of Technology, FutureTech

1

Highlight specific LLM pipeline projects where you integrated document identification, screening, extraction, and classification modules

2

Demonstrate understanding of evidence synthesis workflows by describing how you've previously accelerated systematic reviews

3

Show examples of systems you've refactored for reuse across multiple projects, not just one-off solutions

4

Include metrics on how your human-AI collaboration interfaces improved efficiency or accuracy in previous roles

5

Reference FutureTech's specific research areas (computing progress foundations) and how your skills align with their interdisciplinary approach

✉️ What to Emphasize in Your Cover Letter

['Specific experience with LLM-augmented pipeline development for document processing tasks', 'Examples of designing systems that optimize human-AI collaboration in evidence synthesis contexts', 'Demonstrated ability to create reusable components across multiple projects', 'Understanding of systematic review processes and how to accelerate them through technical solutions']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Review FutureTech's published research on computing progress foundations and AI risk at futuretech.mit.edu
  • → Study MIT's broader AI Risk Initiative goals and current projects
  • → Understand the interdisciplinary nature of FutureTech's work combining economics and technical computing
  • → Look into systematic review methodologies used in AI safety research
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a specific LLM-augmented pipeline you've built for document processing and classification
2 How would you design a human validation process for AI-generated evidence synthesis outputs?
3 Describe your approach to refactoring a system for reuse across different AI risk projects
4 What metrics would you track to measure the effectiveness of your evidence synthesis pipeline?
5 How do you balance automation with human oversight in sensitive AI risk assessment contexts?
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⚠️ Common Mistakes to Avoid

  • Focusing only on theoretical ML knowledge without concrete pipeline development experience
  • Treating this as a generic ML role without addressing the specific evidence synthesis and AI risk context
  • Failing to demonstrate understanding of human-AI collaboration design in systematic review processes

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Massachusetts Institute of Technology, FutureTech!

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