Data/Infrastructure Advocate Engineer - US Remote

About this role

At Hugging Face, we're on a journey to democratize good AI. We're building the fastest-growing platform for AI builders, with over 5 million users and 100k organizations who have shared more than 1M models, 300k datasets, and 300k apps. Our open-source libraries have more than 400k stars on GitHub.

### About the Role

As our first Data/Infrastructure Advocate Engineer, you'll bridge the gap between cutting-edge data infrastructure and the global community of data engineers, researchers, and developers. You'll champion Xet storage on the Hugging Face Hub, helping users efficiently store, version, and collaborate on large-scale datasets. This role is for someone who thrives at the intersection of technical depth (storage, Parquet, deduplication) and community advocacy, helping define the future of open data workflows.

You'll collaborate with teams like Datasets, Hub, and Infrastructure to shape how developers interact with data on our platform, and inspire a community to build better, faster, and more scalable data pipelines.

### Your main missions

• Grow and nurture the open-source data/infra community: launch initiatives, collaborate with data-focused groups, and organize events or challenges. Engage with communities like Apache Parquet, Open Table Formats, and data engineering forums to promote best practices and Hugging Face tools.
• Promote the Hugging Face Hub as the go-to platform for data storage, versioning, and collaboration, curating and showcasing datasets, benchmarks, and tools like Xet.
• Highlight use cases like efficient large-dataset updates, Parquet editing, and deduplication to demonstrate the Hub's value for data workflows.
• Create demos, benchmarks, and tools (for example Colab notebooks) that illustrate best practices for data storage and versioning, and experiment with Xet, Parquet, and other formats.
• Produce high-quality tutorials, blog posts, and videos that make complex topics accessible.
• Share insights on storage optimization, datas...