Member of Technical Staff, Frontiers of Deep Learning Scaling
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The role
Own end-to-end experiments to scale intelligence by scaling compute effectively in large-model pretraining.
Define what to scale up—data quality, continual learning, unified modalities, or novel model architectures—and pursue viable scaling paradigms.
Design and execute large-scale, long-duration training campaigns with stability and efficiency at the core.
Collaborate across data preparation, evaluation, and experiment iteration to drive rapid, reliable progress.
Develop distributed training strategies on multi-GPU hardware using Python, JAX, PyTorch, and Rust.
Communicate findings concisely, maintain rigorous experimentation discipline, and own outcomes from idea to impact.
Define what to scale up—data quality, continual learning, unified modalities, or novel model architectures—and pursue viable scaling paradigms.
Design and execute large-scale, long-duration training campaigns with stability and efficiency at the core.
Collaborate across data preparation, evaluation, and experiment iteration to drive rapid, reliable progress.
Develop distributed training strategies on multi-GPU hardware using Python, JAX, PyTorch, and Rust.
Communicate findings concisely, maintain rigorous experimentation discipline, and own outcomes from idea to impact.
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