Researcher, Interpretability
Conducts research in mechanistic interpretability to understand deep network representations and enhance AI safety. Requires PhD or equivalent in ML/CS, 2+ years research engineering with Python, and passion for safe AGI.
About the job
Responsibilities
- 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.
Requirements
- Excited about OpenAI’s mission of ensuring AGI benefits all of humanity, and aligned with OpenAI’s charter.
- Enthusiasm for long-term AI safety, and deep thought about technical paths to safe AGI.
- Experience in the field of AI safety, mechanistic interpretability, or related disciplines.
- Ph.D. or research experience in computer science, machine learning, or a related field.
- Thrive in environments involving large-scale AI systems, and excited to use OpenAI’s unique resources.
- 2+ years of research engineering experience and proficiency in Python or similar languages.
- Deeply curious.
Skills
Mechanistic Interpretability, Deep Learning, Python, Machine Learning, Ai Safety, Research Engineering, PyTorch, Transformer Models, Neuroscience, LLMs
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