Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline. * Contribute back to the research community via publications, tech blogs, open-source code and contributing to internal/external AI challenges
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