Oct 26, 2024 - Hugging Face is hiring a remote Cloud Machine Learning Engineer. Location: EMEA.
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.
Hugging Face has become the most popular, community-driven project for training, sharing, and deploying the most advanced machine learning models. Workload efficiency is key to our mission of democratizing state of the art and we are always looking to push the boundaries for faster, and more efficient ways to train and deploy models.
About the Role We are looking for a Cloud Machine Learning engineer responsible to help build machine learning solutions used by millions leveraging cloud technologies. You will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers with major cloud platforms or managed SaaS solutions.
Responsibilities Bridging and integrating transformers/diffusers models with different Cloud providers. Ensuring the above models meet the expected performance. Designing & Developing easy-to-use, secure, and robust Developer Experiences & APIs for our users. Write technical documentation, examples, and notebooks to demonstrate new features. Sharing & Advocating your work and the results with the community. About You You'll enjoy working on this team if you have experience with and interest in deploying machine learning systems to production and build great developer experiences. The ideal candidate will have skills including:
Deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, Datasets. Expertise in Deep Learning Framework, preferably PyTorch, optionally XLA understanding. Strong knowledge of cloud platforms like AWS and services like Amazon SageMaker, EC2, S3, CloudWatch and/or Azure and GCP equivalents. Experience in building MLOps pipelines for containerizing models and solutions with Docker. Familiarity with Typescript, Rust, and MongoDB, Kubernetes are helpful. Ability to write clear documentation, examples, and definitions and work across the full product development lifecycle. Bonus: Experience with Svelte & TailwindCSS. 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.
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