Research Engineer, Interpretability
Anthropic
- USA
- Permanent
- Full-time
- 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
- Build infrastructure for running experiments and visualizing results
- Work with colleagues to communicate results internally and publicly
- 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
- High performance, large-scale ML systems
- GPUs, Kubernetes, Pytorch, or OS internals
- Language modeling with transformers
- Reinforcement learning
- Large-scale ETL
- a tool which allows researchers to easily access LLMs internals from a jupyter notebook * ETL pipelines for collecting and analyzing LLM activations at large scale
- Profiling and Optimizing ML Training, including parallelizing to many GPUs
- Make launching ML experiments and manipulating+analyzing the results fast and easy
- Writing a design doc for fault tolerance strategies
- Creating an interactive visualization of attention between tokens in a language model