Research Scientist, Interpretability
Conducts mechanistic interpretability research to reverse-engineer language models, developing methods to understand neural network algorithms for AI safety. Requires scientific research background, Python proficiency, and collaborative engineering mindset.
About the job
Responsibilities
- Develop methods for understanding LLMs by reverse engineering algorithms learned in their weights
- Design and run robust experiments, both quickly in toy scenarios and at scale in large models
- Create and analyze new interpretability features and circuits to better understand how models work
- Build infrastructure for running experiments and visualizing results
- Work with colleagues to communicate results internally and publicly
You may be a good fit if you
- Have a strong track record of scientific research (in any field), and have done some work on Interpretability
- Enjoy team science – working collaboratively to make big discoveries
- Are comfortable with messy experimental science. We're inventing the field as we work, and the first textbook is years away
- You view research and engineering as two sides of the same coin. Every team member writes code, designs and runs experiments, and interprets results
- You can clearly articulate and discuss the motivations behind your work, and teach us about what you've learned. You like writing up and communicating your results, even when they're null
Familiarity with Python is required.
Education requirements: At least a Bachelor's degree in a related field or equivalent experience.
Skills
Python, Mechanistic Interpretability, Neural Networks, LLMs, Transformer Circuits, Experiment Design, Reverse Engineering, Scientific Research, Data Visualization, Infrastructure Development
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