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.
295k – 445k/yr
Hybrid2+ YOEAI Research
About the role
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.
Advances AI products through post-training SOTA LLMs using supervised and reinforcement learning techniques on rich query datasets. Owns data pipelines, training frameworks, and model integration while collaborating across teams. Requires 2-6+ years in large-scale LLMs and Python/PyTorch expertise; PhD preferred.
220k – 485k/yr
On-site2+ YOEAI Research
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Scale AISan Francisco, CA +2
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On-site1+ YOEAI Research
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PolymathSan Francisco, CA
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