Data/Infrastructure Advocate Engineer - US Remote

Hugging Face•New York, NY

2d•Remote

About The Position

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.

Requirements

3+ years in developer relations or developer advocacy, ideally for data engineering, infrastructure, or ML tools and platforms
An established public presence as a technical voice, with a track record of regularly publishing data/infra/ML content and a demonstrable, engaged audience on LinkedIn and X (Twitter)
A portfolio of developer-facing content you can point to: tutorials, blog posts, videos, demos, benchmarks, or conference talks
Hands-on experience building and engaging open-source or developer communities (Discord, GitHub, forums)
Strong Python skills
Hands-on experience with data libraries such as pandas, pyarrow, and huggingface/datasets
Practical experience with storage systems and formats: Parquet, Open Table Formats, and S3
Working knowledge of dataset versioning, deduplication, and compression
Ability to explain complex technical topics clearly through writing, demos, or talks
Fluent written and spoken English

Nice To Haves

Experience with the Hugging Face Hub and datasets ecosystem, or with Xet
Open-source maintainer or contributor experience
Familiarity with large-scale data pipelines and data engineering workflows
Experience producing notebooks (for example Colab) for tutorials and benchmarks

Responsibilities

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, dataset versioning, and deduplication to empower developers.
Actively participate in online communities (Discord, GitHub, forums) to highlight contributions, answer questions, and foster collaboration.
Make sure datasets and tools released on the Hub are well-documented, with clear examples, benchmarks, and use cases.