Cloud Machine Learning Engineer - US remote

Hugging Face; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

A leading company in AI is seeking a Cloud Machine Learning Engineer to build solutions for millions using cloud technologies. The role involves integrating open-source libraries with cloud platforms and ensuring performance standards while advocating for the community. The ideal candidate will have deep experience in machine learning and cloud services, with a focus on building robust developer experiences.

Benefits

Flexible hours

Remote options

Comprehensive benefits

Conference reimbursement

Company equity

Qualifications

• Experience with Hugging Face Technologies like Transformers and Diffusers.
• Strong knowledge of cloud platforms like AWS, Azure, or GCP.

Responsibilities

• Integrating transformers/diffusers models with different Cloud providers.
• Designing & developing secure developer experiences & APIs.

Skills

Machine Learning

Cloud Services

Deep Learning

Documentation

Tools

Docker

AWS

PyTorch

MongoDB

Kubernetes

Job description

Description

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 for helping 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.

To better understand this role, you may want to review these announcements:

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Responsibilities

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

1. Bridging and integrating transformers/diffusers models with different Cloud providers.
2. Ensuring the above models meet expected performance standards.
3. Designing & developing easy-to-use, secure, and robust developer experiences & APIs for our users.
4. Writing technical documentation, examples, and notebooks to demonstrate new features.
5. Sharing & advocating your work and results with the community.

About You

You will enjoy working on this team if you have experience with and interest in deploying machine learning systems to production and building great developer experiences. The ideal candidate will have skills including:

1. Deep experience building with Hugging Face Technologies, including Transformers, Diffusers, Accelerate, PEFT, Datasets.
2. Expertise in Deep Learning Frameworks, preferably PyTorch, and optionally XLA understanding.
3. Strong knowledge of cloud platforms like AWS (SageMaker, EC2, S3, CloudWatch) and/or Azure and GCP equivalents.
4. Experience in building MLOps pipelines for containerizing models with Docker.
5. Familiarity with Typescript, Rust, MongoDB, Kubernetes is helpful.
6. Ability to write clear documentation, examples, and work across the full product development lifecycle.
7. Bonus: Experience with Svelte & TailwindCSS.

More about Hugging Face

We are actively working to build a culture that values diversity, equity, and inclusivity. We aim to create a respectful and supportive workplace for all, regardless of background. Hugging Face is an equal opportunity employer committed to non-discrimination.

We value development, offering reimbursement for conferences, training, and education. We care about your well-being with flexible hours, remote options, and comprehensive benefits. We support our employees wherever they are, providing office visits and workstation setup if needed.

We want our teammates to be shareholders—offering company equity as part of compensation. We also support the community, fostering collaboration across the ML/AI field.