Data/Infrastructure Advocate Engineer - US Remote

Hugging Face • New York, NY

1d • Remote

About The Position

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 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, empowering users to 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

• Have strong technical skills in Python, data libraries (e.g., pandas, pyarrow, huggingface/datasets), and storage systems (Parquet, Open Table Formats, S3).
• Are a hands-on builder who loves experimenting with data tools, storage optimization, and dataset versioning.
• Can clearly explain complex topics (e.g., deduplication, compression, Parquet editing) through writing, demos, or talks.
• Are active in developer communities (GitHub, Discord, forums) and passionate about open source and knowledge sharing.
• Thrive in fast-moving environments and enjoy building in public to inspire others.

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 Tables 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—curate and showcase 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 (e.g., Colab notebooks) to illustrate best practices for data storage and versioning.
• Experiment with Xet, Parquet, and other data formats to showcase their potential for ML and data engineering.
• 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.
• Ensure datasets and tools released on the Hub are well-documented, with clear examples, benchmarks, and use cases.

Benefits

• We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to grow continuously. We provide all employees with reimbursement for relevant conferences, training, and education.
• We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.
• We support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed, and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.
• We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed