Application Guide

How to Apply for Research Lead - Pre-training Safety

at Far.Ai

🏢 About Far.Ai

FAR.AI is a cutting-edge AI safety research organization focused on ensuring the safe development of frontier AI systems. They uniquely emphasize pre-training interventions, aiming to shape model capabilities at their source, which is both technically challenging and socially impactful. Working here means contributing to the future of AI safety in a collaborative, remote-first environment.

About This Role

This role leads FAR.AI's pre-training safety research, specifically scaling methods like Deep Ignorance and exploring novel techniques like gradient routing and data attribution. The work is critical in preventing misuse of open-weight models in high-risk areas like CBRN and cyber, and reducing loss-of-control risks. As Research Lead, you'll direct a team, partner with red teams, and validate methods on >100B parameter models, directly influencing the safety of future AI systems.

💡 A Day in the Life

As Research Lead, you'd start by reviewing progress from your research team, then dive into technical discussions on method design or results analysis. You might collaborate with the red team to evaluate new models, write code for scaling experiments, or mentor junior researchers. Afternoons could involve reading recent papers, presenting findings to stakeholders, and planning next steps for your research roadmap.

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Interview Prep

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🎯 Who Far.Ai Is Looking For

  • PhD or equivalent experience in machine learning, with a strong publication record in areas like LLMs, safety, or interpretability.
  • Proven experience leading research projects, from ideation to implementation, with the ability to mentor junior researchers.
  • Deep technical expertise in pre-training, data filtering, or model editing (e.g., unlearning, gradient routing).
  • Experience with large-scale distributed training (e.g., >10B parameters) and familiarity with red-teaming or adversarial evaluation.

📝 Tips for Applying to Far.Ai

1

Highlight any experience with scaling safety methods to large models, such as Deep Ignorance or similar approaches, and quantify the scale (parameters, tokens).

2

Demonstrate specific knowledge of FAR.AI's research directions by referencing their publications or blog posts on pre-training safety.

3

Showcase your leadership in research by including examples of projects you’ve directed and the outcomes, especially in AI safety.

4

Include a portfolio or links to code, papers, or blog posts that showcase your technical depth in areas like gradient routing or data attribution.

5

Tailor your resume to emphasize your ability to partner with red teams and stress-test models, as this is a key responsibility.

✉️ What to Emphasize in Your Cover Letter

['Express your passion for pre-training safety and why you believe intervening at the source is more effective than post-hoc methods.', 'Highlight your experience with large-scale model training and your familiarity with the specific methods mentioned: data filtering, gradient routing, unlearning, and synthetic data.', "Demonstrate your leadership by citing examples of how you've led research teams and collaborated with cross-functional groups (like red teams).", "Mention your alignment with FAR.AI's mission to reduce risks from open-weight models and your excitement about scaling these methods to frontier systems."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read FAR.AI's recent papers and blog posts on Deep Ignorance and pre-training safety to understand their current methodology and results.
  • → Research the team's background and previous roles to understand their expertise and approach to safety research.
  • → Familiarize yourself with their open-source projects or tools, if any, and consider contributing or providing feedback.
  • → Understand the broader context of AI safety, especially debates on open-weight models and capability control, to align with their mission.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you approach scaling Deep Ignorance to models with >100B parameters? What are the main technical challenges?
2 Describe your experience with gradient routing or similar methods. How would you isolate dual-use capabilities in a MoE model?
3 How do you evaluate the effectiveness of pre-training safety methods? What metrics or benchmarks would you propose?
4 How would you collaborate with a red team to stress-test models and iterate on safety methods?
5 What are the potential limitations of data filtering, and how would you overcome them using data attribution or other techniques?
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Don't focus solely on post-training alignment methods; emphasize your interest and experience in pre-training interventions.
  • Avoid generic leadership examples; instead, provide specific instances where you've driven technical research in AI safety or large-scale ML.
  • Don't underestimate the importance of collaboration with red teams; highlight any experience with adversarial testing or security evaluations.

📅 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!

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Good luck with your application to Far.Ai!

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