AI Safety Fellowship Applications

Master the art of applying to AI safety fellowships and research programs

⏱️ 90 minutesIntermediate

AI Safety Fellowship Applications

Table of Contents

Learning Objectives

  • Understand the landscape of AI safety fellowships and research programs
  • Master the components of compelling fellowship applications
  • Learn to effectively communicate research interests and potential
  • Develop strategies for standing out in competitive application pools
  • Build a portfolio that demonstrates readiness for AI safety research

Introduction

AI safety fellowships represent crucial stepping stones into the field, offering mentorship, funding, and community connections that can launch research careers. These programs—including MATS, SERI MATS, CBAI Fellowship, and others—seek candidates who combine technical competence with genuine safety motivation and research potential.

Success in fellowship applications requires more than strong credentials. It demands clear communication of research interests, demonstration of relevant skills, and authentic engagement with AI safety challenges. This topic provides comprehensive guidance for navigating the fellowship application landscape.

Major AI Safety Fellowship Programs

MATS (ML Alignment Theory Scholars)

Overview: Intensive research program pairing scholars with leading alignment researchers.

Key Features:

  • 3-month intensive program in Berkeley
  • Direct mentorship from established researchers
  • Focus on technical alignment research
  • Funding for living expenses
  • Strong alumni network

What They Look For:

  • Strong ML/CS background or exceptional conceptual thinking
  • Specific research interests aligned with available mentors
  • Evidence of independent research capability
  • Clear motivation for alignment work

CBAI Fellowship (Center for AI Safety)

Overview: Program supporting diverse approaches to AI safety research.

Key Features:

  • Flexible research directions
  • Remote and in-person options
  • Support for both technical and governance work
  • Emphasis on innovative approaches
  • Quarterly cohorts

Application Strengths:

  • Novel research proposals
  • Interdisciplinary backgrounds
  • Clear theory of change
  • Demonstrated initiative

SERI MATS

Overview: Stanford Existential Risk Initiative's alignment research program.

Focus Areas:

  • Technical AI alignment
  • AI governance and strategy
  • Research skill development
  • Collaboration with Stanford researchers

Other Notable Programs

  • Anthropic Fellowship: Focus on empirical safety research
  • DeepMind Scholars: Academic collaboration program
  • FHI Research Scholars: Oxford-based program (FHI closed in April 2024)
  • CAIS Philosophy Fellowship: For philosophy/ethics backgrounds
  • Regional Programs: Various country-specific initiatives

Application Components

Research Statement Excellence

Structure That Works:

  1. Hook (1 paragraph)

    • Start with a specific problem or insight
    • Avoid generic "AI is powerful and dangerous"
    • Show you understand nuanced challenges

  2. Research Interests (2-3 paragraphs)

    • Specific questions you want to explore
    • Connection to existing work
    • Your unique angle or approach
    • Feasible scope for fellowship timeline

  3. Background & Preparation (2 paragraphs)

    • Relevant skills and experience
    • Self-directed learning efforts
    • Previous research or projects
    • Technical competencies

  4. Why This Program (1 paragraph)

    • Specific mentor fit (if applicable)
    • Program structure alignment
    • Community and collaboration goals

  5. Future Direction (1 paragraph)

    • Post-fellowship plans
    • Long-term research vision
    • Contribution to AI safety field

Demonstrating Research Potential

Without Publications:

## Research Artifacts That Matter

1. **Detailed Research Proposals**
   - Well-scoped problems
   - Literature review integration
   - Methodological clarity
   - Novel approaches

2. **Technical Blog Posts**
   - Explain complex concepts clearly
   - Original analysis or synthesis
   - Engagement with current debates
   - Code implementations

3. **Open Source Contributions**
   - AI safety tools or frameworks
   - Reproductions of key papers
   - Documentation improvements
   - Bug fixes showing deep understanding

4. **Course Projects**
   - Extended beyond requirements
   - Safety-relevant applications
   - Independent extensions
   - Clear documentation

5. **Discussion Forum Contributions**
   - LessWrong, Alignment Forum posts
   - Thoughtful comments and critiques
   - Question that show deep engagement

Leveraging Non-Traditional Backgrounds

Philosophy + Technical Skills:

Example positioning: "My philosophy training provides frameworks for analyzing agency, intentionality, and value alignment—concepts central to AI safety. Combined with my ML engineering experience, I can bridge the gap between theoretical safety requirements and practical implementation constraints."

Key Strategies:

  1. Frame as unique value proposition, not deficit
  2. Show concrete applications of philosophical thinking
  3. Demonstrate technical competence through projects
  4. Connect philosophical concepts to technical problems

Other Valuable Combinations:

  • Security + ML: Focus on adversarial robustness
  • Economics + AI: Mechanism design for AI systems
  • Psychology + CS: Human-AI interaction safety
  • Math + Philosophy: Formal verification approaches

Common Application Mistakes

The Generic Safety Concern: ❌ "I want to ensure AI benefits humanity" ✅ "I'm interested in how mesa-optimizers might emerge in large language models, specifically..."

The Credential List: ❌ Listing every course and achievement ✅ Highlighting relevant experiences with clear connections

The Broad Research Interest: ❌ "I want to work on AI alignment" ✅ "I want to explore whether debate protocols can reliably elicit honest behavior from language models"

The Hidden Background: ❌ Downplaying non-CS experience ✅ "My philosophy background uniquely positions me to..."

Interview Preparation

Technical Interviews

Common Topics:

  • ML fundamentals and recent papers
  • Alignment problem understanding
  • Critique of existing approaches
  • Your research proposal details
  • Basic coding/math problems

Preparation Strategy:

  1. Review fundamental ML concepts
  2. Read key papers in your interest area
  3. Practice explaining technical concepts clearly
  4. Prepare specific examples from your work
  5. Think through research methodology

Research Fit Interviews

Key Questions:

  • "Why this specific research direction?"
  • "What would success look like?"
  • "How would you test your hypothesis?"
  • "What are the main obstacles?"
  • "How does this fit into broader safety?"

Strong Responses Show:

  • Concrete thinking about problems
  • Awareness of related work
  • Realistic scope assessment
  • Genuine enthusiasm
  • Intellectual humility

Building Your Application Portfolio

3-Month Preparation Timeline

Month 1: Foundation Building

  • Complete prerequisite courses/reading
  • Start research artifact creation
  • Engage with community forums
  • Identify target programs

Month 2: Portfolio Development

  • Finish 1-2 substantial projects
  • Write technical blog posts
  • Get feedback from community
  • Refine research interests

Month 3: Application Crafting

  • Draft research statements
  • Request recommendations early
  • Revise based on feedback
  • Submit applications early

Research Artifact Examples

Example 1: Technical Blog Post "Analyzing Deceptive Alignment in Small Transformers"

  • Reproduce key results
  • Extend with new analysis
  • Clear visualizations
  • Open source code

Example 2: Research Proposal "Detecting Goal Misgeneralization Through Behavioral Probes"

  • Specific hypothesis
  • Experimental design
  • Expected outcomes
  • Fallback plans

Example 3: Tool Building "Interactive Visualization of Neural Network Interpretability"

  • Practical utility
  • Clean implementation
  • Good documentation
  • Community adoption

Strategic Considerations

Timing Your Applications

Best Practices:

  • Apply to multiple programs
  • Stagger applications across cycles
  • Have backup plans
  • Continue building portfolio between applications

Red Flags to Avoid:

  • Last-minute applications
  • No specific research direction
  • Weak technical background with no mitigation plan
  • No evidence of AI safety engagement

Making Your Application Stand Out

Unique Value Propositions:

  1. Interdisciplinary Insights

    • "My economics background helps me analyze incentive structures in multi-agent systems"

  2. Technical + Conceptual

    • "I combine strong implementation skills with philosophical rigor"

  3. Specific Expertise

    • "My cryptography experience applies directly to privacy-preserving alignment techniques"

  4. Novel Approaches

    • "I propose using methods from computational linguistics to understand model deception"

Demonstrating Genuine Interest

Authentic Engagement Signals:

  • Specific papers that influenced you
  • Particular problems that fascinate you
  • Time invested in self-study
  • Community contributions
  • Original thoughts on problems

Avoiding "Safety Theater":

  • Don't name-drop without understanding
  • Don't claim interests you don't have
  • Don't hide your actual motivations
  • Don't pretend expertise you lack

Success Stories and Patterns

Profile: Philosophy → AI Safety

Background: Philosophy PhD, self-taught ML Key Success Factors:

  • Created blog series on "Philosophical Foundations of AI Alignment"
  • Built simple but insightful ML experiments
  • Connected philosophy expertise to concrete safety problems
  • Showed continuous learning trajectory

Profile: Software Engineer → Researcher

Background: Industry SWE, no research experience Key Success Factors:

  • Contributed to major AI safety open source projects
  • Wrote detailed technical critiques of papers
  • Built tools used by other researchers
  • Demonstrated deep conceptual understanding

Profile: Pure Math → Applied Safety

Background: Math degree, minimal programming Key Success Factors:

  • Focused on formal verification approaches
  • Learned enough coding to implement ideas
  • Collaborated with technical partners
  • Brought unique theoretical perspectives

Conclusion

Fellowship applications succeed when they demonstrate genuine engagement, clear thinking, and unique value. Your non-traditional background isn't a weakness—it's what makes your perspective valuable. Focus on building concrete demonstrations of your capabilities, engage authentically with the community, and articulate clearly how you'll contribute to AI safety.

Remember: reviewers want to find promising researchers. Make it easy for them to see your potential by being specific, demonstrating capability, and showing authentic motivation.

Resources

Application Resources

  • Previous successful applications (anonymized)
  • Fellowship program websites
  • Community advice threads
  • Mentor matching guides

Skill Building

  • AI Safety Fundamentals course
  • Technical tutorial sequences
  • Research methodology guides
  • Writing workshop resources

Community Engagement

  • LessWrong and Alignment Forum
  • AI Safety Discord/Slack channels
  • Local AI safety meetups
  • Online reading groups
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