You will have access to our massive GPU cluster for training and inference. Some core technologies we use include Python, torch, diffusers, and the fal Python SDK. You'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs. You have work published in ICCV, ICML, Neurips, CVPR.

What we offer at fal includes interesting and challenging work, competitive salary and equity, a lot of learning and growth opportunities, health, dental, and vision insurance (US), regular team events and offsites.

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Your work will focus on developing, fine-tuning, and operationalizing machine learning models to enhance user experiences. Sometimes your work will require entirely novel training or architecture developments. While other times it will require fine-tuning pre-existing models with novel datasets.

You will have access to our massive GPU cluster for training and inference.

Some core technologies we use include Python, Torch, Diffusers, and the Fal Python SDK.

You'll work alongside a team dedicated to quickly iterating on and deploying new AI breakthroughs.

Our compensation package includes a salary range of $170,000 - $250,000, plus equity, and a comprehensive benefits package.

We offer interesting and challenging work, a lot of learning and growth opportunities, visa sponsorship, and relocation assistance to San Francisco.

We also provide health, dental, and vision insurance, regular team events, and offsites.

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You will work on integrating Hugging Face's open-source libraries like Transformers and Diffusers, with major cloud platforms or managed SaaS solutions. This role involves bridging and integrating models with different cloud providers, ensuring the models meet expected performance, designing and developing easy-to-use, secure, and robust developer experiences and APIs for our users, writing technical documentation, examples and notebooks to demonstrate new features, and sharing and advocating your work and the results with the community.

The ideal candidate will have 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, and bonus experience with Svelte & TailwindCSS.

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.

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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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