Application Guide

How to Apply for Research Scientist

at Principles of Intelligence

🏢 About Principles of Intelligence

Principles of Intelligence is a remote-first research lab focused on diversifying AI safety research by tackling key bottlenecks from multiple scientific angles. Their emphasis on physics-grounded models and mechanistic interpretability makes them a unique destination for researchers seeking to bridge fundamental science with practical AI alignment.

About This Role

As a Research Scientist, you'll develop data structure models and synthetic datasets to benchmark AI interpretability tools, with a strong focus on physics-theory-grounded approaches. Your work will directly quantify how AI features relate to underlying data structures, advancing mechanistic interpretability in a scalable, tractable manner.

💡 A Day in the Life

Your typical day might start with a literature review or coding a new synthetic dataset based on a spin model, then collaborating with team members over Slack to refine the benchmark design. Afternoons could involve running experiments to quantify how a transformer encodes the data structure, followed by writing up results or brainstorming next steps in a whiteboard session.

🚀 Application Tools

Generate Cover Letter

AI-powered, tailored for Principles of Intelligence

Interview Prep

Practice questions for this role

🎯 Who Principles of Intelligence Is Looking For

  • Has a PhD or equivalent research experience in physics, mathematics, or a related quantitative field, with a track record of interdisciplinary work.
  • Demonstrates hands-on experience in mechanistic interpretability research, such as feature visualization, probing, or circuit analysis.
  • Strong programming skills in Python and familiarity with ML frameworks (e.g., PyTorch) for building and analyzing neural network models.
  • Able to translate concepts from statistical physics or dynamical systems into ML interpretability contexts, e.g., using spin models or renormalization group ideas.

📝 Tips for Applying to Principles of Intelligence

1

Highlight any prior work that uses physics-inspired models (e.g., Hopfield networks, tensor networks) to understand neural network representations.

2

If you have published on synthetic datasets for interpretability, explicitly mention how they were designed to test specific hypotheses about feature learning.

3

Tailor your research statement to show how you would approach quantifying the relationship between data structure and learned features, using concrete examples.

4

Mention experience with benchmarking interpretability tools (e.g., completeness, minimality, or faithfulness metrics) and how you would improve them.

5

Since the company is remote, emphasize your ability to work independently and collaborate asynchronously, with examples of past remote projects.

✉️ What to Emphasize in Your Cover Letter

['Explain your motivation for bridging physics and AI safety, referencing specific physics concepts (e.g., symmetry, phase transitions) that you believe can inform interpretability.', 'Describe a past project where you built or analyzed a synthetic dataset to isolate a specific data structure and measured how a neural network encoded it.', "Connect your research to the company's mission of diversifying AI safety research, showing how your background offers a fresh perspective.", 'Mention any experience with mechanistic interpretability tools (e.g., activation patching, sparse autoencoders) and how you would extend them using physics-grounded models.']

Generate Cover Letter →

🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read the company's publications or blog posts on their website (princint.ai) to understand their current research directions.
  • → Study their past work on synthetic data benchmarks for interpretability, if any, to see how your approach could complement it.
  • → Explore the broader AI safety landscape to understand where physics-grounded interpretability fits in, e.g., work by Anthropic or DeepMind on feature decomposition.
  • → Familiarize yourself with key papers on mechanistic interpretability that use physics analogies, such as 'Toy Models of Superposition' or 'Softmax Linear Units'.
Visit Principles of Intelligence's Website →

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you design a synthetic dataset to test whether a neural network's features correspond to underlying physical symmetries?
2 Describe a tractable physics model that could serve as a data structure for benchmarking interpretability tools.
3 How do you quantify the relationship between a feature learned by a neural network and the generating data structure? Give a specific metric.
4 What are the key bottlenecks in current mechanistic interpretability, and how can physics help overcome them?
5 Explain a time you conducted interdisciplinary research—how did you bridge two fields and what challenges did you face?
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Don't submit a generic application; ensure every part of your resume and cover letter directly addresses the physics-interpretability intersection.
  • Avoid overpromising on deliverables—this is a research role, so focus on your approach and questions rather than claiming to solve AI safety.
  • Don't neglect to show concrete programming skills; include links to relevant code repositories or projects demonstrating your ability to build ML models and analyze data.

📅 Application Timeline

⏰ Deadline: June 21, 2026

We recommend applying at least a few days early to avoid last-minute technical issues.

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 Principles of Intelligence!

← Back to Job Listing Apply Now →