Research Engineer, Interpretability
Research Engineer focused on mechanistic interpretability, building tools and infrastructure to reverse-engineer neural networks for safer AI. Requires 5+ years software experience, Python proficiency, and AI research contributions.
About the job
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
Skills
Python, Rust, Go, Java, PyTorch, Transformers, Gpus, Jupyter, Distributed Systems, Machine Learning
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