Open-Source Machine Learning Engineer - US Remote

Hugging Face•New York, NY

1d•Remote

About The Position

As an Open-Source Machine Learning Engineer at Hugging Face, you will play a crucial role in enhancing the open-source machine learning ecosystem. Your primary focus will be on existing open-source libraries such as Transformers, Datasets, PyTorch, and vLLM. You will engage with users and contributors across the broader open-source ML community. The role involves brainstorming to align your work with your interests and impactful contributions, fostering a vibrant machine learning community, and assisting users in contributing to and utilizing the tools you develop. You will collaborate daily with researchers, ML practitioners, and data scientists through platforms like GitHub, Hugging Face 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.
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.