Research Engineer, Interpretability

Responsibilities

• Implement and analyze research experiments, both quickly in toy scenarios and at scale in large models
• Set up and optimize research workflows to run efficiently and reliably at large scale
• Build tools and abstractions to support rapid pace of research experimentation
• Develop and improve tools and infrastructure to support other teams in using Interpretability’s work to improve model safety

You may be a good fit if you

• Have 5-10+ years of experience building software
• Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with python
• Have some experience contributing to empirical AI research projects
• Have a strong ability to prioritize and direct effort toward the most impactful work and are comfortable operating with ambiguity and questioning assumptions
• Prefer fast-moving collaborative projects to extensive solo efforts
• Want to learn more about machine learning research and its applications and collaborate closely with researchers
• Care about the societal impacts and ethics of your work

Strong candidates may also have experience with

• Designing a code base so that anyone can quickly code experiments, launch them, and analyze their results without hitting bugs
• Optimizing the performance of large-scale distributed systems
• Collaborating closely with researchers
• Language modeling with transformers
• GPUs or Pytorch

Representative Projects

• Building Garcon, a tool that allows researchers to easily access LLMs internals from a jupyter notebook
• Setting up and optimizing a pipeline to efficiently collect petabytes of transformer activations and shuffle them
• Profiling and optimizing ML training, including parallelizing to many GPUs
• Make launching ML experiments and manipulating+analyzing the results fast and easy
• Creating an interactive visualization of attention between tokens in a language model