Open-Source Machine Learning Engineer - US Remote

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.

Responsibilities

Work to improve the open-source machine learning ecosystem; Mainly 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, helping users contribute to and use the tools you build; Work with researchers, ML practitioners, and data scientists every day through GitHub, 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; 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

Nice to have

JAX or TensorFlow a plus; 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

Benefits

Reimbursement for relevant conferences, training, and education; Flexible working hours and remote options; Health, dental, and vision benefits for employees and their dependents; Parental leave; Flexible paid time off; Company equity as part of compensation package; Workstation setup; Opportunity to visit offices in NYC and Paris