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Machine Learning Engineer Internship, Hardware Optimization - EMEA 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.

About the Role

At Hugging Face, we’re leading the AI revolution with a mission to democratize machine learning. Through our open-source libraries, state-of-the-art models, and curated datasets, we empower developers and researchers to build cutting-edge AI solutions. Besides, to ensure our models run seamlessly across a diverse range of hardware platforms, our ML Optimization team partners with some of the world’s top hardware innovators, including AWS Inferentia and Trainium, AMD CPUs and Instinct GPUs, Nvidia GPUs, Google TPUs, Intel CPUs, and Habana accelerators.

At the heart of these collaborations is Optimum, our open-source library that bridges the Hugging Face ecosystem with specific hardware. Optimum and its sub-packages are pivotal in optimizing performance and accessibility, helping developers maximize efficiency and scalability.

As an intern on the ML Optimization team, you’ll play a key role in shaping the future of AI. Your contributions will involve developing and refining cutting-edge solutions for widely-used and emerging hardware platforms, sharing these advancements with the Hugging Face community, and enabling researchers and developers worldwide to access the best tools and technologies. This is your opportunity to make a tangible impact on the AI landscape while working alongside world-class experts and forward-thinking hardware providers.

Key Responsibilities

1. Develop an online exporter tool: Create a user-friendly online tool to convert Hugging Face models for specific hardware platforms leveraging Optimum.

2. Bake the recipes: Author comprehensive guides to help users deploy Hugging Face models on various hardware platforms, including detailed instructions and best practices.

3. Design User Flow: Develop a seamless flow to guide users from traditional Hugging Face libraries (like Transformers and Diffusers) to alternative hardware backends. This includes integrating these solutions into the Hugging Face Hub and our partners' platforms.

4. Optimize Hardware Selection: Conduct inference experiments across different hardware backends to identify the strengths and weaknesses of each platform under various scenarios. Provide clear guidelines to help users select the best hardware for their specific tasks.

5. Advocate and Communicate Insights: Collaborate with the Hugging Face Advocacy team to share your findings and insights through various channels, including blog posts, tweets, leaderboards, Spaces, and YouTube videos. You will educate and inspire the community about the importance of hardware in AI.

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 support our employees wherever they are. While we have office spaces around the world, especially in the US, Canada, and Europe, 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 support the community. We believe significant scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.

Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.

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What You Should Know About Machine Learning Engineer Internship, Hardware Optimization - EMEA Remote, Hugging Face

Are you ready to jump into the exciting world of AI? At Hugging Face, we're seeking a passionate Machine Learning Engineer Intern for Hardware Optimization to join our vibrant, remote team across the EMEA region! As part of our mission to democratize AI, you'll become an essential player on our ML Optimization team, collaborating with top hardware innovators like AWS, AMD, Nvidia, and more. You’ll dive into developing and refining cutting-edge solutions for popular and emerging hardware platforms, while empowering developers worldwide with accessible tools and technologies. Your key responsibilities will include creating a user-friendly online exporter tool to assist in converting Hugging Face models for specific hardware, authoring comprehensive guides that detail the deployment process, and optimizing hardware selection by conducting inference experiments. You'll also collaborate with our Advocacy team to share your insights with the community through engaging content. This is not just about learning; it’s about making a lasting impact on the AI landscape. At Hugging Face, we celebrate diversity and inclusivity, valuing each team member's contributions. We provide a flexible work environment, support your growth with educational reimbursements, and encourage a culture of continuous learning. If you’re excited to work alongside some of the brightest minds in technology and contribute to meaningful advancements in AI, this internship is the perfect stepping stone for your career!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Internship, Hardware Optimization - EMEA Remote Role at Hugging Face
What are the responsibilities of a Machine Learning Engineer Intern at Hugging Face?

As a Machine Learning Engineer Intern specializing in Hardware Optimization at Hugging Face, you'll take on several exciting responsibilities. Your primary tasks will involve developing an online tool to convert Hugging Face models for various hardware platforms, writing detailed guides for deploying these models, and optimizing hardware selection through inference experiments. You'll also work with the Advocacy team to share insights and knowledge with the broader community, enhancing the understanding of hardware capabilities in AI development.

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What qualifications do you need to apply for the Machine Learning Engineer Internship at Hugging Face?

To apply for the Machine Learning Engineer Internship at Hugging Face, you should have a solid foundation in machine learning and familiarity with both software development and hardware capabilities. Ideally, you’ll have experience with programming languages such as Python, knowledge of machine learning frameworks, and an understanding of AI hardware. Strong communication skills are crucial since you'll be collaborating with different teams and sharing your insights with the community.

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How does Hugging Face support its employees in professional development?

Hugging Face is committed to the growth and development of its employees. As a Machine Learning Engineer Intern, you will have access to reimbursement for relevant conferences, training, and educational opportunities. The culture at Hugging Face emphasizes continuous learning, encouraging innovation and collaboration so that you can refine your skills and contribute to groundbreaking AI solutions.

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What is the significance of Optimum in the Machine Learning Engineer Internship at Hugging Face?

Optimum is central to the work of a Machine Learning Engineer Intern at Hugging Face. It is an open-source library that links our ecosystem with specific hardware platforms, allowing for the optimization of performance and accessibility. In this role, you’ll leverage Optimum to develop tools and guides that help users deploy AI models effectively on various hardware, enhancing both efficiency and scalability for the developer community.

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What should a candidate include in their cover letter for the Machine Learning Engineer Internship at Hugging Face?

In your cover letter for the Machine Learning Engineer Internship at Hugging Face, it's important to express your passion for open-source and AI. Highlight your relevant skills and experiences, particularly those related to machine learning and hardware optimization. Discuss specific topics you’re interested in working on, and convey your eagerness to contribute to the Hugging Face community while learning from industry experts.

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Common Interview Questions for Machine Learning Engineer Internship, Hardware Optimization - EMEA Remote
Can you explain your experience with machine learning frameworks?

Be sure to discuss the frameworks you've worked with, highlighting specific projects where you've applied them. Mention your comfort level with libraries like TensorFlow, PyTorch, or Hugging Face's Transformers. Sharing experiences or challenges you overcame using these frameworks can demonstrate your practical knowledge.

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How do you approach hardware optimization for machine learning models?

Explain your process for optimizing models, including any experimentation with different hardware backends. Discuss tools or libraries like Optimum that you might use and how you've analyzed performance differences across platforms. Providing specific examples of your optimization strategies will be beneficial.

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What interests you about the open-source community?

Share your enthusiasm for collaboration, knowledge sharing, and the impact of open-source on innovation. Discuss how working in an open-source environment fosters growth and learning, and touch on any contributions you've made to open-source projects.

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How do you prioritize tasks when working on multiple projects?

Discuss your time management strategies, such as using task management tools or creating a priority matrix. Illustrate this by providing examples from previous experiences where you've successfully juggled multiple responsibilities, emphasizing your ability to meet deadlines without compromising quality.

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What metrics do you consider most important when assessing model performance?

Talk about metrics like accuracy, precision, recall, F1 score, and inference time. Explain why these metrics matter and how they influence the decisions you make regarding model development and optimization.

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Describe a challenging problem you've solved in machine learning.

Share a specific story that details the problem, your approach to finding a solution, and the outcome. Highlight the skills and tools you used, as well as any collaboration with team members or mentors during the process.

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How do you stay current with trends in AI and machine learning?

Mention sources such as academic journals, online courses, and AI conferences that you follow. Highlight any active participation in AI forums or communities, such as contributing to discussions or working on collaborative projects.

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What is your understanding of the importance of documentation in machine learning?

Emphasize the necessity of maintaining clear, understandable documentation for reproducibility and collaboration. Mention how well-documented models and codebases enhance project efficiency and serve as a reliable reference for future development.

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How do you effectively communicate complex technical concepts to non-technical team members?

Discuss your strategies for simplifying jargon and using analogies or visuals to explain concepts clearly. Emphasize the importance of adapting your communication style based on your audience’s understanding.

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What tools do you use to conduct experiments and analyze results?

List tools such as Jupyter Notebooks, TensorBoard, or any other data visualization software you've used to track model performance. Discuss how these tools help you analyze results and iterate on experiments effectively.

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Internship, remote
DATE POSTED
December 7, 2024

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