Application Guide

How to Apply for Research Engineer

at FAR AI

🏢 About FAR AI

FAR AI is a unique non-profit that bridges the gap between academic research and industry, focusing on AI safety and trustworthiness. By working here, you'll contribute to high-impact research that might otherwise be underfunded, while collaborating with leading researchers in a mission-driven environment.

About This Role

As a Research Engineer, you'll turn cutting-edge ML research into scalable code, run experiments to validate ideas, and contribute to major open-source libraries like PyTorch and HuggingFace. Your work directly enables scientific discoveries and will be credited in published papers, making a tangible impact on AI safety.

💡 A Day in the Life

You'll start by reviewing recent research papers, then dive into coding—implementing a new algorithm or optimizing an existing codebase. You'll run experiments on a cluster, analyze results, and discuss findings with the team. Afternoons might involve contributing to an open-source project or writing up a section of a paper, with regular syncs to align on priorities.

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Interview Prep

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🎯 Who FAR AI Is Looking For

  • Strong software engineering skills with deep experience in Python and ML frameworks (PyTorch, HuggingFace Transformers), able to write efficient, scalable code for research.
  • Proven track record of contributing to open-source ML projects, with clear examples of pull requests or issue resolutions.
  • Comfortable with scientific experimentation: designing and running ablation studies, analyzing results, and iterating based on findings.
  • Excellent academic writing skills, with experience co-authoring papers or technical reports in ML or related fields.

📝 Tips for Applying to FAR AI

1

Highlight your open-source contributions: link to your GitHub or specific PRs to PyTorch, HuggingFace, or similar projects.

2

Showcase a research project where you implemented an algorithm from a paper and ran experiments; describe the impact of your work.

3

Tailor your resume to emphasize both engineering (scalability, optimization) and research (experimentation, analysis) skills.

4

In your cover letter, mention a specific FAR AI project or research agenda that excites you and how your skills align.

5

Prepare to discuss a time you debugged a complex ML pipeline or improved the performance of a model in production.

✉️ What to Emphasize in Your Cover Letter

["Your passion for AI safety and how FAR AI's mission aligns with your career goals.", 'Specific examples of open-source contributions and how they improved usability or performance.', 'Experience running scientific experiments and deriving insights from data.', 'Your ability to communicate complex technical ideas clearly, both in code and in writing.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read FAR AI's published papers and blog posts to understand their research focus (e.g., adversarial robustness, interpretability).
  • → Explore their GitHub repositories to see their coding style and open issues where you could contribute.
  • → Understand the broader AI safety landscape and key organizations (e.g., MIRI, Anthropic, OpenAI) to contextualize FAR's role.
  • → Familiarize yourself with the latest developments in PyTorch and HuggingFace libraries, especially around distributed training.
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through a recent ML paper and how you would implement it; discuss potential scalability issues.
2 Design an experiment to test a hypothesis about model behavior (e.g., robustness to distribution shift).
3 How would you contribute to an open-source project like PyTorch? Give a concrete example of a feature or bug fix.
4 Tell us about a time you had to balance speed and correctness in a research codebase.
5 What is your understanding of AI alignment and why is it important? How does this role contribute?
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⚠️ Common Mistakes to Avoid

  • Applying without showing genuine interest in AI safety; don't just mention it—demonstrate knowledge.
  • Overemphasizing model training over engineering; this role needs strong coding and system design skills.
  • Ignoring the open-source requirement; not having a public portfolio or GitHub profile can be a dealbreaker.

📅 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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