About the Team
The Scaling Laws group within Architecture works to advance our foundational understanding of our deep learning stack. We study the algorithmic scaling behavior of pre-training across architecture, optimization, and data. Besides leading directly to capabilities improvements, this work underpins OpenAI's ability to carry out other research through predictive scaling laws, correct experimental methodology, and evaluations.
About the Role
As a Research Scientist here, you will be responsible for conducting investigations and developing principled theories that predict algorithmic scaling behavior. You will build and apply theoretically-grounded intuitions, design and execute careful experiments, think deeply about hyperparameters, and deliver improvements across all aspects of our Pre-training algorithms.
You might thrive in this role if:
You have a background in statistical machine learning, physics, mathematics, or another theoretically and empirically rigorous field.
Or, you have a strong track record in deep learning research demonstrating an expert knowledge of deep learning algorithms (objectives, architecture, optimization).
You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time.
You are a capable programmer and are comfortable implementing novel deep learning algorithms correctly and autonomously.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
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