The opportunity
The Interpretability team studies internal representations of deep learning models. We are interested in using representations to understand model behavior, and in engineering models to have more understandable representations.
What you'll do
Develop and publish research on techniques for understanding representations of deep networks.
Engineer infrastructure for studying model internals at scale.
Collaborate across teams to work on projects that OpenAI is uniquely suited to pursue.
Guide research directions toward demonstrable usefulness and/or long-term scalability.
Are excited about OpenAI’s mission of ensuring AGI benefits all of humanity,: and are aligned with OpenAI’s charter .
Show enthusiasm for long-term AI safety & alignment, and have thought deeply: about technical paths to safe AGI.
What they're looking for
- Bring experience in the field of AI safety & alignment, mechanistic: interpretability, or spiritually related disciplines.
- Hold a Ph.D. or have research experience in computer science, machine learning, or a related field.
- Thrive in environments involving large-scale AI systems, and are excited to: make use of OpenAI’s unique resources in this area.
- Possess 2+ years of research engineering experience and proficiency in Python or similar languages.
