Cloud Machine Learning Engineer - US remote

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 300k apps. Our open-source libraries have more than 400k+ stars on Github.

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.

You may want to take a look at these announcements to get a better sense of what this role might mean in practice 🤗:

Responsibilities

We are looking for talented people with deep experience and passion for both Machine Learning (at the framework level) and Cloud Services:

• Bridging and integrating 🤗 transformers/diffusers models with a different Cloud provider.
• 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 definition 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.