Manage and scale cloud compute infrastructure for machine learning training and inference, automating ML pipelines and CI/CD while supporting reliable, reproducible model operations across the ML lifecycle.
Design rigorous AI benchmarks and dataset standards, build evaluation infrastructure with R&D, and turn reproducible results into clear guidance for model development and customer-facing proof points.
Conduct research and hands-on experiments on attention-based foundation models, including distributed training, architecture improvements, and designing new tasks and benchmarks to advance the project.
Submit a spontaneous application to explore opportunities contributing to Pathwayβs AI technology and data processing engine, with hands-on experience helping scale an AI company.
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