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 founded in July 2022 that takes a portfolio approach to AI safety, running diverse bets across the safety stack rather than betting on a single direction. With 50+ staff, 40+ academic papers, and publications at NeurIPS, ICML (including a Best Paper Honorable Mention in 2026), and ICLR, it offers serious infrastructure—a dedicated engineering team and compute cluster—so researchers can focus on research. Working here means contributing to independent, publicly shared safety research that informs frontier labs and governments.
About This Role
As a Research Scientist in Applied White-Box Methods, you will develop and apply interpretability techniques that directly inspect model internals—such as circuit analysis, activation probing, and mechanistic interventions—to understand and mitigate risks in advanced AI systems. This role is impactful because white-box methods provide the deepest level of understanding needed to diagnose deceptive alignment, backdoors, and other failure modes that black-box evaluations might miss. You will work on a portfolio of safety bets, from early experiments to deployment, often in red-team partnerships with frontier labs.
💡 A Day in the Life
A typical day might involve running experiments on the compute cluster to test a new activation patching technique on a frontier model, analyzing results to identify safety-relevant circuits, and then meeting with engineers to scale the method. You might also collaborate with red-team partners to apply your findings to real-world model audits, or spend time writing up results for a paper or public report. The balance shifts between deep technical work and strategic discussions about which safety bets to prioritize.
🚀 Application Tools
🎯 Who Far.Ai Is Looking For
- PhD in computer science, machine learning, or a related field, with a strong publication record in interpretability, mechanistic analysis, or white-box safety methods at top venues like NeurIPS, ICML, or ICLR.
- Hands-on experience implementing and scaling white-box techniques (e.g., activation patching, causal tracing, sparse autoencoders, circuit discovery) on large language models or other deep neural networks.
- Ability to work independently in a remote, non-profit research environment, balancing exploratory research with applied deployment goals and collaborating with engineering teams on infrastructure.
- Demonstrated interest in AI safety and a track record of translating interpretability findings into actionable safety recommendations, ideally through red-team collaborations or public reports.
📝 Tips for Applying to Far.Ai
Highlight any experience with white-box interpretability methods in your CV and cover letter—name specific techniques (e.g., activation patching, causal scrubbing, SAEs) and the models you applied them to.
Reference FAR.AI's portfolio approach and explain how your research fits into their mission; show you understand they run multiple bets and value both early-stage and deployment-oriented work.
Include links to your GitHub or code repositories that demonstrate reproducible white-box experiments, as FAR.AI values engineering rigor and open science.
Mention any collaborations with frontier labs or government red-team exercises, since FAR.AI emphasizes partnerships and real-world adoption of safety research.
In your application, propose a concrete white-box research direction you could pursue at FAR.AI—this shows initiative and alignment with their need for independent researchers who can drive projects.
✉️ What to Emphasize in Your Cover Letter
Emphasize your specific expertise in white-box methods and how you've applied them to safety-relevant problems. Explain why FAR.AI's non-profit, independent structure appeals to you and how you'd contribute to their portfolio of safety bets. Highlight any experience working with large-scale models or compute clusters, as FAR.AI provides serious infrastructure and expects researchers to leverage it. Finally, connect your work to their goal of advancing global understanding of AI risks and solutions, showing you can translate technical findings into broader impact.
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read FAR.AI's published papers, especially any on interpretability or white-box methods, to understand their technical contributions and style.
- → Explore their website and blog to learn about their portfolio projects, red-team partnerships, and events, so you can speak to how your work aligns with their theory of change.
- → Look into the backgrounds of their research staff and collaborators to identify potential overlaps with your expertise and potential mentors.
- → Review their compute infrastructure and engineering stack (if publicly described) to understand the resources available and how you might leverage them.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Focusing only on black-box evaluations or high-level safety philosophy without demonstrating hands-on white-box technical skills.
- Ignoring FAR.AI's non-profit, independent structure and treating the role like a typical industry research position—show you value public benefit and open science.
- Submitting a generic application that doesn't reference specific FAR.AI projects or their portfolio approach, indicating a lack of genuine interest in the organization.
📅 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!