About The Position

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. 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.

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 (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 Discord)
  • Fluent written English for asynchronous collaboration across a distributed, global community
  • Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.

Nice To Haves

  • Experience maintaining an open-source project
  • Prior contributions to Transformers, Datasets, Accelerate, or similar libraries
  • Familiarity with distributed training, inference optimization, or GPU/accelerator performance work
  • Experience training or fine-tuning models at scale

Responsibilities

  • Improve the open-source machine learning ecosystem.
  • Work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM.
  • Interact with users and contributors across the broad open-source ML ecosystem.
  • Help foster one of the most active machine learning communities.
  • Help users contribute to and use the tools you build.
  • Work with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack.

Benefits

  • Reimbursement for relevant conferences, training, and education
  • Flexible working hours
  • Remote options
  • Health, dental, and vision benefits for employees and their dependents
  • Parental leave
  • Flexible paid time off
  • Company equity as part of their compensation package
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