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 2M models, 700k datasets & 600k apps. Our open-source libraries have more than 600k+ stars on Github. Hugging Face has become the most popular, community-driven project for training, sharing, and deploying the most advanced machine learning models.
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 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
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