Open-Source Machine Learning Engineer - International Remote
New York, New York, United States - Remote
Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.Here at Hugging Face, we’re on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better.
We have built the fastest-growing, open-source, library of pre-trained models in the world. With more than 1 Million+ models and 320K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA.
About the Role
As an open-source Machine Learning Engineer, you will work to improve the open-source machine learning ecosystem. You will mainly work with existing open-source libraries, such as Transformers, Datasets, or Accelerate, and you will interact with users and contributors of the broad open-source machine learning 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 get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build. You'll interact with Researchers, ML practitioners and data scientists on a daily basis through GitHub, our forums, or slack.
About you
If you love open-source, are passionate about making complex technology more accessible, and want to contribute to one of the fastest-growing ML ecosystems, then we can't wait to see your application!
If 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 background complement one another. We're happy to consider where you might be able to make the biggest impact.
More about Hugging Face
We 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.
We 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.
We 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.
We 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. If needed, we'll also outfit your workstation to ensure you succeed.
We want our teammates to be shareholders. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.
We support the community. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.
Requirements
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.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: GitHub Machine Learning Open Source Salesforce Transformers
Perks/benefits: Career development Conferences Equity / stock options Flex hours Flex vacation Health care Parental leave Team events
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