What this interview will probe
Adapts and optimizes large language and vision models for efficient training and inference on Cerebras systems, researching architectures, sparsity, and numerical techniques that exploit wafer-scale compute. Publishes and partners with engineering to bring research into the production model stack. A technical interview would probe transformer internals, training-at-scale tradeoffs (parallelism, memory, precision), and the math behind an optimization or sparsity method you would propose.
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