Application Guide
How to Apply for Research Lead, Pre-Training Safety
at FAR AI
🏢 About FAR AI
FAR AI is a nonprofit research incubator that tackles AI safety problems too resource-intensive for academia but not yet commercially viable for industry. They focus on high-impact, long-term research agendas like pre-training safety, making them a unique bridge between academic freedom and frontier-scale resources. Working here means shaping the foundational safety of future AI systems without the pressure of product deadlines.
About This Role
As Research Lead for Pre-Training Safety, you'll pioneer methods to remove harmful capabilities from models before they are trained, using techniques like data filtering and gradient routing. You'll build and lead a team to scale these methods to frontier models, conduct novel research on isolating dual-use capabilities, and collaborate with red teams to stress-test safety. This role is critical for reducing loss-of-control risks in advanced AI systems.
💡 A Day in the Life
A typical day might involve leading a team meeting to review progress on scaling gradient routing to a new frontier model, debugging data filtering pipelines, and analyzing red team feedback to refine safety methods. You'll also spend time mentoring researchers, reading latest papers, and strategizing with collaborators on how to isolate dual-use capabilities. Expect a mix of hands-on technical work and high-level research planning.
🚀 Application Tools
🎯 Who FAR AI Is Looking For
- Deep expertise in pre-training dynamics, data curation, and large-scale model training, with hands-on experience in methods like data filtering or gradient routing.
- Proven track record of leading research teams and scaling methods to frontier-scale models, ideally in a safety-focused context.
- Strong publication record in machine learning, AI safety, or related fields, with novel contributions to capability removal or model interpretability.
- Experience collaborating with red teams and analyzing safety method scalability to superhuman systems, demonstrating a proactive safety mindset.
📝 Tips for Applying to FAR AI
Highlight specific projects where you removed or isolated harmful capabilities from models, detailing the techniques (e.g., gradient routing) and scale (e.g., billions of parameters).
Emphasize leadership experience: quantify team sizes, mentorship outcomes, and how you scaled research from prototype to production.
Show familiarity with FAR AI's research agenda by referencing their work on Deep Ignorance or similar pre-training safety methods in your cover letter.
Demonstrate ability to bridge research and engineering by describing collaborations with red teams or cross-functional partners.
Include links to code repositories, papers, or blog posts that showcase your hands-on contributions to pre-training safety or related areas.
✉️ What to Emphasize in Your Cover Letter
["Your vision for pre-training safety at frontier scale and how you would advance FAR AI's mission.", 'Concrete examples of leading teams to implement data filtering or gradient routing in large models.', 'Experience stress-testing models with red teams and translating findings into scalable safety methods.', 'Your approach to isolating dual-use capabilities and reducing loss-of-control risks in superhuman AI systems.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FAR AI's published research, especially any papers on Deep Ignorance, data filtering, or gradient routing, to understand their technical approach.
- → Explore their project portfolio and blog to identify current challenges and strategic priorities in pre-training safety.
- → Investigate the backgrounds of FAR AI's team and advisors to understand their expertise and research culture.
- → Look into recent advancements in pre-training safety from other organizations (e.g., Anthropic, OpenAI) to contextualize FAR AI's unique contributions.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on post-training safety methods (e.g., RLHF) without addressing pre-training interventions, which is the core of this role.
- Overemphasizing academic publications without demonstrating hands-on experience in scaling methods to large models.
- Neglecting to show leadership or team-building experience, as this is a lead role requiring both research and management skills.
📅 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:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!