採用
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.
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 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.b 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.
About you
You’re a great fit if you:
- 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.
If you're interested in joining us but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact.
More about Hugging Face
**We are actively working to build a culture that values diversity, equity, and inclusivity.**We are intentionally building a workplace where you feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community, as well as the future of machine learning more broadly. Hugging Face is an equal opportunity employer, and we do not discriminate based on race, ethnicity, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or ability status.
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.
Total Views
0
Apply Clicks
0
Mock Applicants
0
Scraps
0
Similar Jobs

Corporate Solutions Engineer
Postman · New York, New York, United States

Lighting Tech/Engineer
Live Nation · New York, NY, USA
CE
Staff Cloud Infrastructure Software Engineer
Celonis · New York, US, New York

Staff Design-Engineer | Design Systems
Ramp · New York, NY (HQ)

Partner Solutions Engineer
Notion · New York, New York
About Hugging Face

Hugging Face
Series DHugging Face, Inc., is an American company based in New York City that develops computation tools for building applications using machine learning.
201-500
Employees
New York City that develops computation
Headquarters
$4.5B
Valuation
Reviews
3.3
2 reviews
Work Life Balance
3.0
Compensation
3.0
Culture
2.0
Career
2.5
Management
1.8
15%
Recommend to a Friend
Cons
Poor hiring process with lack of human element
AI-driven candidate assessment lacking personal touch
No constructive feedback after extensive work submissions
Salary Ranges
15 data points
Junior/L3
Mid/L4
Senior/L5
Staff/L6
Junior/L3 · Machine Learning Engineer
1 reports
$130,000
total / year
Base
$100,000
Stock
-
Bonus
-
$130,000
$130,000
Interview Experience
10 interviews
Difficulty
3.2
/ 5
Duration
14-28 weeks
Offer Rate
10%
Experience
Positive 10%
Neutral 40%
Negative 50%
Interview Process
1
Application Review
2
Resume Screening
3
Technical Interview
4
Online Assessment
5
Virtual Onsite Interview
6
Team Matching
7
Offer
Common Questions
Machine Learning/AI Concepts
Coding/Algorithm
Technical Knowledge
System Design
Behavioral/STAR
News & Buzz
Hugging Face used to spread Android trojan TrustBastion - SecurityBrief Australia
Source: SecurityBrief Australia
News
·
5w ago
Why AI start-up Hugging Face turned down a $500mn Nvidia deal - Financial Times
Source: Financial Times
News
·
5w ago
deepseek-ai/DeepSeek-OCR-2 · Hugging Face
·
5w ago
·
334
·
43
zai-org/GLM-4.7-Flash · Hugging Face
·
7w ago
·
752
·
230