Application Guide
How to Apply for Research Scientist, Applied White-Box Methods
at Far.Ai
🏢 About Far.Ai
FAR.AI is a non-profit AI research institute dedicated to ensuring advanced AI is safe and beneficial, with a unique portfolio approach that spans from early theory to real-world deployment. They offer serious infrastructure—a dedicated engineering team and compute cluster—so researchers can focus on high-impact safety work. Their rapid growth, top-tier publications (NeurIPS, ICML, ICLR), and partnerships with frontier labs and governments make it a standout place for safety-focused scientists.
About This Role
As a Research Scientist in Applied White-Box Methods, you'll develop and apply interpretability and mechanistic understanding techniques to real-world AI systems, likely in collaboration with red-team partners and frontier labs. This role is impactful because it bridges foundational white-box research with practical safety evaluations, directly informing how we audit and improve advanced AI. You'll work remotely with a mission-driven team, contributing to publications and tools that set new safety standards.
💡 A Day in the Life
You might start by reviewing experiment results from a white-box probing run on a frontier model, then meet with engineers to optimize the pipeline for scale. Later, you'd collaborate with red-team partners to interpret findings, document insights for a paper, and brainstorm new methods to detect hidden risks. Expect a mix of coding, analysis, and cross-team discussions—all remote and focused on high-impact safety outcomes.
🚀 Application Tools
🎯 Who Far.Ai Is Looking For
- PhD or equivalent research experience in machine learning, interpretability, or a related field, with a strong publication record at venues like NeurIPS, ICML, or ICLR.
- Deep expertise in white-box methods such as mechanistic interpretability, circuit analysis, probing, or causal interventions on neural networks.
- Experience applying interpretability techniques to large language models or other frontier AI systems, ideally in a safety or red-team context.
- Strong engineering skills to implement experiments at scale, collaborate with infra teams, and translate research into deployable tools.
📝 Tips for Applying to Far.Ai
Highlight any work where you applied white-box methods to real models (not just toy datasets) and explain the safety implications.
Mention specific FAR.AI publications or projects (e.g., their red-team partnerships or ICML 2026 paper) and how your skills could extend that work.
Emphasize your ability to work independently in a remote setting while contributing to a portfolio of diverse safety bets.
Include links to code repositories or demos that showcase your interpretability implementations, as FAR.AI values practical, scalable research.
In your application, articulate how your research aligns with FAR.AI's mission and non-profit, public-benefit approach—not just academic curiosity.
✉️ What to Emphasize in Your Cover Letter
Focus on: (1) your specific experience with white-box methods applied to frontier models, including any safety-relevant outcomes; (2) how you've collaborated with engineering teams to scale experiments; (3) your motivation to work at a non-profit that prioritizes impact over profit, with examples of public-facing research; and (4) a concrete idea for a white-box project that would advance FAR.AI's portfolio, showing you understand their theory of change.
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FAR.AI's recent publications, especially those involving white-box methods or red-teaming, to understand their technical direction and tone.
- → Explore their portfolio approach by reviewing their website and blog to see how they balance foundational and applied research across different safety bets.
- → Look into their partnerships with frontier labs and governments (e.g., red-team collaborations) to understand the real-world context of the role.
- → Check out their events and communications to grasp how they influence policy and public understanding, which may inform your cover letter.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Submitting a generic application that doesn't mention FAR.AI's non-profit mission or specific white-box projects—they want mission-aligned researchers.
- Focusing only on theoretical interpretability without demonstrating experience applying methods to large-scale models or safety use cases.
- Neglecting to show engineering capability; FAR.AI provides infra, but researchers are expected to implement and scale their own experiments.
📅 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!