At Hugging Face, we're on a journey to democratize good AI.
\nWe are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 4 million models, 1 million datasets & 1.5 million Gradio apps.
\nOur open-source libraries have more than 700,000 stars on Github.
\nRoles at Hugging Face are very fluid and dynamic -- we're looking for someone who is comfortable taking on different challenges that evolve over time.
\nThis role sits on the Xet Storage team, the group responsible for the storage system behind all of Hugging Face.
\nToday we store over 200PB (and growing rapidly!) of the world's most important ML & AI assets.
\nXet is the underlying storage architecture for the entire Hugging Face platform and community -- from the largest model repositories to the datasets and Spaces that millions of people build on every day.
\nIn this role, you'll work across two closely connected surfaces.
\nYou'll contribute to xet-core, our open-source project written in Rust that powers hf-xet -- the Python library underpinning the Hugging Face Hub client and the wider ecosystem of open-source tools the community relies on.
\nAnd you'll design, build, and operate meaningful and challenging features in the Xet Storage backend, contributing to the broader Infrastructure organization at Hugging Face.
\nWe're a small team building and operating incredible things at enormous scale, in a high-trust, low-process, async, and remote environment -- and we lean heavily on the latest AI tools to move fast.
\nIf you love writing low-level, high-performance code and are energized by the challenge of large-scale, scalable services, this is the team for you.
\nThis usually means proven experience building and operating production systems software, but we consider every applicant on an individual basis.
\nRequirements:
\nBonus points if you have:
\nBenefits:
\nWe're on a journey to democratize good AI at Hugging Face, building the fastest growing platform for AI builders. The Xet Storage team is responsible for the storage system behind all of Hugging Face, storing over 200PB of ML & AI assets.
\nIn this role, you'll work on the Xet Storage team, contributing to xet-core, our open-source project written in Rust, and designing, building, and operating features in the Xet Storage backend.
\nResponsibilities:
\nRequirements:
\nBenefits:
\nWe're on a journey to democratize good AI. We are looking for a Software Engineer to help develop Gradio, a Python framework that lets users build machine learning web apps, and Trackio, a local-first, agent-friendly experiment tracking platform.
\nKey responsibilities include designing, developing, and maintaining core features for Gradio and Trackio, ensuring scalability, reliability, and ease of use for ML developers. You will also architect complex Python projects and frameworks that serve as foundational tools for the ML community.
\nCollaboration with open-source contributors and engagement with the broader community through code reviews, discussions, and support are also essential. Additionally, you will integrate modern frontend technologies to create seamless and intuitive user experiences for Python-based web applications.
\nAdaptability to evolving engineering challenges and contribution to the direction of our developer tools as the landscape changes are required.
\nIf you love open-source, are passionate about making machine learning more accessible, have experience building Python frameworks, and want to contribute to one of the fastest-growing machine learning libraries, then we can't wait to see your application!
","enriched_at":1780477575095},{"id":"job_bbd59ce5-e8d","title":"Software Engineer","source_url":"https://apply.workable.com/j/CB1DEFE6CE","location":"Paris","job_type":"full-time","experience_level":"senior","work_arrangement":"remote","category":"Engineering","description":"We're on a journey to democratize good AI. As a Software Engineer at Hugging Face, you will help develop Gradio, a Python framework that lets users build machine learning web apps, and Trackio, a local-first, agent-friendly experiment tracking platform. Your focus will be on designing, developing, and maintaining core features for Gradio and Trackio, ensuring scalability, reliability, and ease of use for ML developers.
\nKey responsibilities include:
\nRequirements:
\nIf you're interested in joining us, but don't tick every box above, we still encourage you to apply!
","enriched_at":1780477566357},{"id":"job_b46b3008-077","title":"Open-Source Machine Learning Engineer","source_url":"https://apply.workable.com/j/19A136F8E2","location":"Paris","job_type":"full-time","experience_level":"mid","work_arrangement":"remote","category":"Engineering","description":"At Hugging Face, we're on a journey to democratize good AI. 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.
\nYou'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.
\nWe're looking for someone with a public track record of open-source work, who enjoys collaborating with a community out in the open on GitHub. You should love open source, be passionate about making complex technology more accessible, and want to contribute to one of the fastest-growing ML ecosystems.
\nKey responsibilities include:
\nNice to have:
\nWe're on a journey to democratize good AI. 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.
\nYou'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.
\nYou 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.
\n