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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 🤗:
Hugging Face and AWS partner to make AI more accessible
Hugging Face and IBM partner on watsonx.ai, the next-generation enterprise studio for AI builders
Introducing SafeCoder
Hugging Face Collaborates with Microsoft to launch Hugging Face Model Catalog on Azure

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.

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What You Should Know About Cloud Machine Learning Engineer - US remote, Hugging Face

Are you passionate about the intersection of cloud technology and machine learning? Join Hugging Face as a Cloud Machine Learning Engineer and help us democratize AI! At Hugging Face, we’re cultivating a thriving community of over 5 million AI builders, empowering users with cutting-edge open-source libraries like Transformers and Diffusers. In this remote role, you’ll dive deep into cloud platforms to integrate our powerful machine learning solutions, reaching millions across the globe. You'll be responsible for creating seamless, user-friendly APIs, ensuring our models perform at their best, and enhancing our developer experiences. Your work will help shape how AI is utilized across industries, pushing boundaries and improving how models are built and deployed. If you’re excited about contributing to a diverse culture that values inclusivity and supports ongoing learning and growth, you'll fit right in! Enjoy flexible hours, remote options, and the chance to work with some of the brightest minds in the field. Plus, all employees get company equity, offering a chance to share in our success as we redefine the landscape of machine learning and AI. If you believe in the power of collaboration and community in driving scientific advancements, we want to hear from you!

Frequently Asked Questions (FAQs) for Cloud Machine Learning Engineer - US remote Role at Hugging Face
What are the main responsibilities of a Cloud Machine Learning Engineer at Hugging Face?

As a Cloud Machine Learning Engineer at Hugging Face, you'll focus on integrating our open-source libraries with major cloud platforms, developing easy-to-use APIs, ensuring optimal model performance, and creating technical documentation that shares your innovative work with the community.

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What skills are required for the Cloud Machine Learning Engineer position at Hugging Face?

Ideal candidates for the Cloud Machine Learning Engineer role at Hugging Face should have extensive experience with Hugging Face technologies, deep learning frameworks like PyTorch, cloud platforms such as AWS or Azure, and familiarity with MLOps pipelines, Docker, and coding in Typescript or Rust.

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How does Hugging Face promote a diverse and inclusive workplace for Cloud Machine Learning Engineers?

Hugging Face is committed to creating a workplace where everyone feels valued, respected, and included. We actively encourage diversity and inclusivity, ensuring our team of Cloud Machine Learning Engineers has a wide range of backgrounds and perspectives, which fosters creativity and innovation.

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What is the work-life balance like for a Cloud Machine Learning Engineer at Hugging Face?

Hugging Face offers a flexible working environment, allowing Cloud Machine Learning Engineers to manage their schedules effectively. With remote work options and an emphasis on work-life balance, you’ll find it easy to maintain personal and professional commitments.

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What benefits does Hugging Face offer to Cloud Machine Learning Engineers?

Cloud Machine Learning Engineers at Hugging Face enjoy a comprehensive benefits package, including health, dental, and vision insurance, parental leave, flexible paid time off, and reimbursement for educational conferences and training.

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Is prior experience in AI necessary for the Cloud Machine Learning Engineer position at Hugging Face?

While prior experience in AI is certainly beneficial for the Cloud Machine Learning Engineer position at Hugging Face, we value diverse skills and backgrounds. Candidates with solid cloud and software engineering expertise, along with a passion for machine learning, are highly encouraged to apply.

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What is the company's vision for AI and how does the Cloud Machine Learning Engineer contribute?

Hugging Face aims to democratize AI, making it accessible for everyone. As a Cloud Machine Learning Engineer, you play a crucial role in achieving this vision by developing cutting-edge solutions that empower millions of users and enhance the efficiency of machine learning applications.

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Common Interview Questions for Cloud Machine Learning Engineer - US remote
Can you describe your experience with integrating machine learning models into cloud environments?

To answer this, highlight specific projects where you utilized cloud services, discussing the challenges you faced and the solutions you implemented. Emphasize your familiarity with cloud platforms such as AWS, Azure, or GCP, and mention specific tools or frameworks you used in the process.

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What strategies do you use to optimize the performance of machine learning models?

Discuss techniques like hyperparameter tuning, model selection, and performance monitoring. Provide examples from your past experiences, detailing how these strategies led to improved efficiency or accuracy of the models deployed.

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How do you ensure that documentation for APIs or tools you develop is clear and helpful?

Explain your approach to writing documentation, which should include drafting comprehensive user guides and examples. Mention any tools you use for documentation and highlight the importance of receiving feedback from users to continuously improve clarity and usability.

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Describe a challenge you faced while working on an MLOps pipeline and how you overcame it.

Share a specific challenge related to the automation or deployment of models you encountered. Describe your thought process in identifying the root cause and the steps you took to resolve it. Emphasize the skills you applied in troubleshooting and collaboration.

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What cloud-based tools have you used for monitoring and maintaining machine learning models in production?

Mention tools that you’ve employed for monitoring model performance and infrastructure health, such as AWS CloudWatch, Azure Monitor, or custom-built solutions. Share how these tools have improved your workflow and model reliability.

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Can you give an example of how you have collaborated with cross-functional teams in past projects?

Discuss how you've worked with teams across different disciplines, such as software engineering, product management, and data science. Highlight specific projects where cooperation was key to success, focusing on your role in bridging these teams.

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How do you stay updated on the latest trends and advancements in machine learning and cloud technologies?

Share your methods for keeping current with developments in the field, such as attending conferences, participating in online forums, or taking courses. This shows your commitment to personal and professional growth in a rapidly evolving industry.

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What is your experience with containerization technologies like Docker in machine learning projects?

Discuss your hands-on experience with Docker, mentioning specific projects where you containerized applications for deployment. Talk about the advantages you observed in using Docker for model scalability and maintainability.

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What are your thoughts on the importance of security in machine learning implementations?

Emphasize the critical role of security in handling sensitive data and models. Discuss strategies you've used to secure APIs and cloud resources, as well as how best practices help mitigate potential risks in deploying machine learning solutions.

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Describe your process for debugging machine learning models in production.

Share your systematic approach to identifying and fixing issues with models in production environments. Explain how data validation, logging, and testing play a role in your debugging process, ensuring you maintain model performance.

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Full-time, remote
DATE POSTED
November 29, 2024

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