Open-Source Machine Learning Engineer - US Remote

At Hugging Face , we're on a journey to democratize good AI. We're building the fastest-growing platform for AI builders, with over 11 million users who have shared more than 2M models, 700k datasets, and 600k apps. Our open-source libraries have more than 600k stars on GitHub. About the Role As an Open-Source Machine Learning Engineer, you'll work to improve the open-source machine learning ecosystem. You'll mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM, and you'll interact with users and contributors across the broad open-source ML ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. You'll help foster one of the most active machine learning communities, helping users contribute to and use the tools you build. You'll work with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack. About You You have a public track record of open-source work, and you enjoy collaborating with a community out in the open on GitHub. You love open source, you're passionate about making complex technology more accessible, and you want to contribute to one of the fastest-growing ML ecosystems. If that's you, we can't wait to see your application. What you'll need Strong Python skills, with experience writing clean, well-tested, maintainable library code Deep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus) Practical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries A public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub Solid understanding of modern machine learning and deep learning, including transformer architectures Experience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Dis