{"id":35004,"url":"https://alion.io/job/hugging-face-open-source-machine-learning-engineer-emea-remote","title":"Open-Source Machine Learning Engineer - EMEA Remote","company":{"id":21,"name":"Hugging Face","domain":"huggingface.co","url":"https://alion.io/company/hugging-face","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France"],"countries":["FR"],"hiring_countries":["FR","DE","AE","GB","AT","BE","BA","BG","HR","CY","CZ","DK","EG","FI","HU","IE","IL","IT","KE","NL","NG","NO","PL","RO","SA","RS","ZA","ES","SE","CH","TR","UA","AL","DZ","AD","AO","BH","BJ","BW","BF"],"hiring_countries_total":115,"salary":null,"salary_estimate":{"min_usd":50000,"max_usd":107000,"period":"year","method":"role_country_seniority_unknown","sample_n":107},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Accelerate","optional":false},{"name":"Discord","optional":false},{"name":"GitHub","optional":false},{"name":"Gradio","optional":false},{"name":"Hugging Face","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Slack","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"vLLM","optional":false},{"name":"Fine-tuning","optional":true}],"status":"live","first_seen_at":"2026-05-29T00:00:00Z","employer_posted_date":"2026-05-29","last_verified_at":"2026-10-17T11:51:20Z","board_verified":true,"closed_at":null,"days_open":141,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":141},"description":"At Hugging Face, we're on a journey to democratize good AI. We 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. Our open-source libraries have more than 700,000 stars on Github.\nAbout the Role\nAs 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.\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.\nAbout You\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. If that's you, we can't wait to see your application.\nWhat you'll need\nStrong Python skills, with experience writing clean, well-tested, maintainable library code\nDeep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus)\nPractical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries\nA public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub\nSolid understanding of modern machine learning and deep learning, including transformer architectures\nExperience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord)\nFluent written English for asynchronous collaboration across a distributed, global community\nNice to have\nExperience maintaining an open-source project\nPrior contributions to Transformers, Datasets, Accelerate, or similar libraries\nFamiliarity with distributed training, inference optimization, or GPU/accelerator performance work\nExperience training or fine-tuning models at scale\nA note on fit\nIf you're interested in joining us but don't tick every box above, we still encourage you to apply. We're building a diverse team whose skills, experiences, and backgrounds complement one another, and we're happy to consider where you might make the biggest impact.\nOne more thing\nAt Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “GPU-poor and proud ”. No trick, no catch, it just tells us a real person is on the other side.\nMore about Hugging Face\nWe are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported-regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.\nWe value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.\nWe care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.\nWe support our employees wherever they are. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. 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