AI Jobs & Internships at Hugging Face 2026
Hugging Face is the platform that hosts the world's largest collection of open-source AI models and datasets — over 600,000 models and 100,000 datasets — and provides the Transformers, Diffusers, and PEFT libraries that are foundational to modern AI development. Called the 'GitHub of AI', Hugging Face has become central infrastructure for AI research and application development. The company is highly distributed with team members across dozens of countries.
AI Roles at Hugging Face
ML Engineer
Research Scientist
Open Source Developer
Developer Advocate
Enterprise Solutions Engineer
LLM Evaluation Researcher
Fine-Tuning Engineer
Developer Experience Engineer
Work Culture at Hugging Face
Hugging Face has a genuinely open-source-first culture where most work is done publicly on GitHub and through community engagement. The company practices radical transparency — most research is public and most engineering discussions happen in public GitHub issues. Employees are expected to be active community members, not just internal contributors. The culture is inclusive, globally distributed, and values accessibility — making AI tools usable by the broadest possible community is a genuine cultural value, not just a mission statement.
How to Get a Job at Hugging Face
- 1.
The most effective way to get hired at Hugging Face is to already be an active contributor to their open-source libraries — contributions to Transformers, Diffusers, or PEFT are directly visible to hiring managers
- 2.
Your Hugging Face profile with model uploads, Space demos, and dataset contributions serves as a de facto portfolio
- 3.
Writing popular technical blog posts or educational content on machine learning topics that Hugging Face shares demonstrates the communication skills they value highly
- 4.
Engagement with the open-source community — answering questions in forums, reviewing pull requests — signals the collaborative mindset they hire for
- 5.
The interview process typically includes a take-home project with a Hugging Face library, followed by technical discussion — be prepared to discuss your contribution history