Open-Source Machine Learning Engineer - US Remote
CHECKED 12H AGO
Infrastructureremote
At a glance
Highlights
- Improve the open-source ML ecosystem used by millions of builders.
- Foster one of the most active machine learning communities in the open.
- Help democratize good AI and make complex technology more accessible.
What they offer
- Company equity for all employees
- Health, dental and vision benefits for employees and dependents
- Flexible working hours and flexible paid time off
- Parental leave
- Reimbursement for conferences, training and education
- Workstation setup support and office visit opportunities
Ideal candidate
Open-source enthusiast with a public track record of collaboration on GitHub who loves making complex technology accessible. You thrive in async, community-driven work and want to grow one of the fastest-growing ML ecosystems.
Requirements
- Strong Python skills, with experience writing clean, well-tested, maintainable library code
- Deep hands-on experience with a modern deep-learning framework, especially PyTorch
- Practical experience with the Hugging Face open-source stack or comparable ML libraries
- Public track record of open-source contributions reviewable on GitHub
- Solid understanding of modern machine learning and deep learning, including transformer architectures
- Experience collaborating with a technical community in the open
- Fluent written English for asynchronous collaboration
- remote policy
- Remote within the US
- hiring process
- Submit a cover letter on why you want to work in open-source at Hugging Face and start first application answer with GPU-poor and proud.
Lab profile
About Hugging Face
Open platform democratizing AI with shared models, datasets, and apps for builders
What they build
- Model Hub with 4M+ models
- Dataset Hub with 1M+ datasets
- 1.5M Gradio apps
Known for
- Fastest-growing platform for AI builders
- 11M+ user community
- 700k+ GitHub stars
- Open-source mission to democratize good AI
Key facts
- 11M+ platform users
- 4M+ models, 1M+ datasets, 1.5M apps shared