Application Guide

How to Apply for Teacher

at Technical Alignment Research Accelerator

🏢 About Technical Alignment Research Accelerator

TARA is a nonprofit uniquely focused on building AI safety talent in Asia-Pacific, a region critical for global AI governance. By joining TARA, you'll directly contribute to accelerating careers of researchers who will shape the future of AI alignment, working remotely with a mission-driven team.

About This Role

As a Teacher, you'll guide 25-30 participants through a 14-week intensive AI safety program, delivering advanced ML lectures and hands-on support. This role is impactful because you'll equip the next generation of AI safety researchers with technical skills and ecosystem knowledge, directly addressing the talent gap in alignment research.

💡 A Day in the Life

A typical day might involve reviewing participant code on Slack in the morning, preparing lecture slides on transformer circuits for the upcoming Saturday, and hosting a 1-hour pair programming session to help a participant debug a RL environment. You'll also spend time reading recent AI safety papers to stay current for mentoring discussions.

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Interview Prep

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🎯 Who Technical Alignment Research Accelerator Is Looking For

  • Deep expertise in advanced ML: reinforcement learning, transformers, mechanistic interpretability, and model evaluation (e.g., published research or significant project experience).
  • Proven teaching or mentoring experience in AI safety or related technical fields (e.g., TA for ML courses, leading workshops, or supervising research interns).
  • Strong remote collaboration skills, including async communication (Slack, GitHub) and live facilitation (Zoom, pair programming).
  • Ability to provide constructive technical feedback on project proposals, with an eye for research novelty and feasibility.

📝 Tips for Applying to Technical Alignment Research Accelerator

1

Tailor your resume to highlight specific expertise in each listed ML topic (RL, transformers, mechanistic interpretability, model evaluation) with concrete examples.

2

In your cover letter, explicitly mention your experience teaching or mentoring, and describe a specific instance where you helped someone learn a complex ML concept.

3

Prepare a brief teaching demo (e.g., a 5-minute explanation of a mechanistic interpretability concept) and be ready to share it if asked.

4

Show your familiarity with the AI safety ecosystem by referencing key organizations (e.g., MIRI, Anthropic, DeepMind) and current research directions.

5

Mention your availability for 4pm Saturday sessions and any experience with time zones across Asia-Pacific.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your passion for AI safety and alignment, and why you want to contribute to talent development in Asia-Pacific.', 'Highlight specific technical expertise in RL, transformers, mechanistic interpretability, and model evaluation with examples from your work.', 'Describe your teaching philosophy and experience, especially in remote or accelerator-style programs.', 'Show your ability to provide constructive feedback on research proposals, possibly referencing a past mentoring experience.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read TARA's website thoroughly, including their program structure and past participant outcomes.
  • → Familiarize yourself with the AI safety curriculum used by TARA (e.g., ARENA, AGISF, or similar programs).
  • → Look into recent publications from TARA's team or alumni to understand their research focus.
  • → Understand the AI safety landscape in Asia-Pacific, including key conferences (e.g., EAGx) and organizations.
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Explain a mechanistic interpretability technique (e.g., activation patching) to a non-expert.
2 How would you help a participant debug a reinforcement learning training instability issue via Slack?
3 Describe your approach to reviewing a project proposal on model evaluation. What criteria would you use?
4 What current AI safety research directions are you most excited about, and why?
5 How do you handle a participant who is struggling with the technical material while balancing asynchronous support?
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Don't apply if you lack hands-on experience with transformers or mechanistic interpretability—the role requires deep technical depth.
  • Avoid generic teaching experience (e.g., high school math) without connecting it to advanced ML or AI safety.
  • Don't underestimate the time commitment for Saturday sessions and async support; be clear about your availability.

📅 Application Timeline

⏰ Deadline: August 8, 2026

We recommend applying at least a few days early to avoid last-minute technical issues.

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!

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Good luck with your application to Technical Alignment Research Accelerator!

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