Open-Source Machine Learning Engineer

Join our team as an Open-Source Machine Learning Engineer, where you'll work to improve the open-source machine learning ecosystem. You'll primarily focus on existing open-source libraries such as Transformers, Datasets, Pytorch, and vLLM, and interact with users and contributors across the broad open-source ML ecosystem. You'll have the opportunity to collaborate with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack. Enjoy a flexible work environment, health insurance, unlimited PTO, equity, and generous parental leave.

• Contribuer à l'amélioration des bibliothèques open-source existantes, telles que Transformers, Datasets, Pytorch et vLLM.
• Interagir avec les utilisateurs et les contributeurs de l'écosystème ML open-source, en les aidant à utiliser les outils développés.
• Collaborer avec des chercheurs, des praticiens de l'IA et des scientifiques des données au quotidien via GitHub, nos forums et Slack.
• 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
• Solid understanding of modern machine learning and deep learning, including transformer architectures
• Practical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries
• Deep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus)
• Fluent written English for asynchronous collaboration across a distributed, global community
• A public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub
• Experience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord)
• Strong Python skills, with experience writing clean, well-tested, maintainable library code
• Familiarity with distributed training, inference optimization, or GPU/accelerator performance work
• Experience training or fine-tuning models at scale
• Prior contributions to Transformers, Datasets, Accelerate, or similar libraries
• Experience maintaining an open-source project
• If you're interested in joining us but don't tick every box above, we still encourage you to apply