Application Guide

How to Apply for Research Lead, Pre-training Safety

at FAR.AI

🏢 About FAR.AI

FAR.AI is a nonprofit AI safety research organization focused on preventing misuse and loss-of-control risks from advanced AI systems. Unlike many labs, FAR.AI prioritizes safety over commercial interests, offering a rare chance to work on pre-training safety at scale without product pressures. The remote-first, mission-driven culture attracts top researchers who want to directly reduce catastrophic risks from frontier models.

About This Role

As Research Lead for Pre-training Safety, you will direct research on removing harmful capabilities from AI models during pre-training, targeting models >100B parameters. You'll partner with red teams to stress-test capability-control methods, build and mentor a technical team, and stay hands-on with coding and experiments. This role is pivotal for developing novel data filtering, gradient routing, unlearning, and synthetic data integration techniques to make future AI systems safer by design.

💡 A Day in the Life

A typical day might involve reviewing experiment results from your team's latest pre-training run, debugging a gradient routing implementation, and meeting with red team partners to plan stress tests. You might also mentor a junior researcher on data filtering techniques, contribute code to a training pipeline, and draft a section of a paper on a novel unlearning method. The balance leans heavily on technical execution and team coordination, all within a remote-first setup.

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Interview Prep

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🎯 Who FAR.AI Is Looking For

  • Strong research track record in AI with deep hands-on experience in language-model pre-training, dataset construction, and large-scale training pipelines (e.g., >100B parameter models).
  • Proven leadership or mentorship experience, having built or guided technical teams and set research direction in AI safety or related areas.
  • Established publication record in AI safety, particularly on topics like unlearning, data filtering, gradient routing, or capability control.
  • Comfortable balancing high-level strategy with hands-on coding and experimentation, and experienced in collaborating with red teams to validate safety methods.

📝 Tips for Applying to FAR.AI

1

Highlight specific pre-training projects you've led, including model sizes, datasets, and safety interventions (e.g., data filtering, unlearning) you implemented.

2

Emphasize any experience collaborating with red teams or adversarial testing groups, as this is a key partnership in the role.

3

Demonstrate leadership by quantifying team growth, mentorship outcomes, and research direction you've set—not just management titles.

4

Reference FAR.AI's published research or blog posts in your application to show genuine alignment with their mission and technical approach.

5

If you have open-source contributions or code from large-scale training pipelines, include links to GitHub or similar to prove hands-on capability.

✉️ What to Emphasize in Your Cover Letter

Your cover letter should emphasize: (1) your direct experience in pre-training safety at scale, including specific techniques like data filtering or gradient routing; (2) your leadership in building and mentoring technical teams while staying technically active; (3) your track record of publishing novel safety research and collaborating with red teams; and (4) why FAR.AI's nonprofit, safety-first approach appeals to you over industry labs.

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read FAR.AI's recent publications and blog posts to understand their current research thrusts in pre-training safety and capability control.
  • → Investigate their team members' backgrounds and past work to identify potential collaborators and align your experience with their expertise.
  • → Explore their stance on AI safety risks and how they differentiate from other organizations like Anthropic or DeepMind's safety teams.
  • → Look for any public talks, podcasts, or interviews with FAR.AI leadership to grasp their culture, remote work practices, and strategic priorities.
Visit FAR.AI's Website →

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you design a pre-training pipeline to remove harmful capabilities from a 100B+ parameter model while preserving general performance?
2 Describe your experience with unlearning or gradient routing techniques and how you validated their effectiveness against red team attacks.
3 How do you balance research direction with hands-on coding—give an example of a time you led a team while contributing technically.
4 What novel approaches to synthetic data integration for safety have you explored, and what were the results?
5 How would you prioritize research directions in pre-training safety to address loss-of-control risks in advanced AI systems?
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Focusing only on high-level strategy without demonstrating hands-on coding or experimentation experience in pre-training.
  • Neglecting to mention specific safety techniques (e.g., unlearning, data filtering) or metrics from past work, which makes your application generic.
  • Showing lack of familiarity with FAR.AI's nonprofit mission or treating the role like a standard industry research position.

📅 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!

Ready to Apply?

Good luck with your application to FAR.AI!

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