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ML Research Engineer Internship, Multimodal - 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.

About the Role

At Hugging Face, we're dedicated to democratizing machine learning and making cutting-egg models accessible to everyone. We focus on developing open-source tools and models that push the boundaries of AI while remaining efficient and user-friendly.

Aligned with this, we've recently released SmolVLM [1], a state-of-the-art, fully open-source VLM that's small, fast, and memory-efficient. SmolVLM stands out for its ability to run on limited computational resources, making it deployable on local setups like laptops and edge devices. This opens up new possibilities for reducing inference costs and enabling user customization.

As an intern on the SmolVLM project, you will be at the forefront of multimodal AI innovation. Your responsibilities will include: • Developing and Optimizing Vision Language Models: Collaborate with our team to enhance the SmolVLM architecture. You'll improve its efficiency, memory footprint, and performance, ensuring it remains a leading model given its compact size.

• Training Models on Our High-Performance Computing Cluster: Use our cluster with 100s of H100s to train and fine-tune SmolVLM models on large-scale, open-source datasets like The Cauldron [2] and Docmatix [3].

• Research and Experimentation: Engage in cutting-edge research to explore new techniques in multimodal learning. You'll experiment with context extension and efficient image encoding.

This internship offers a unique opportunity to immerse yourself in developing accessible, high-performance AI models. You'll gain practical experience with advanced machine-learning techniques and contribute to projects that have a tangible impact on the AI community.

Checkout hf.co/science for more information about the science team at Hugging Face.

1] https://huggingface.co/blog/smolvlm

[2] https://huggingface.co/datasets/HuggingFaceM4/the_cauldron/

[3] https://huggingface.co/datasets/HuggingFaceM4/Docmatix

About You

If you love open-source but also have an eye for art and creativity, are passionate about making complex technology more accessible to engineers and artists, and want to contribute to one of the fastest-growing ML ecosystems, then we can't wait to see your application!

If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and background complement one another. We're happy to consider where you might be able to make the biggest impact.

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 ML Research Engineer Internship, Multimodal - US Remote, Hugging Face

At Hugging Face, we’re excited to welcome a new ML Research Engineer Intern for our groundbreaking SmolVLM project. This is not just any internship; it’s your chance to dive deep into the world of multimodal AI innovation right from the comfort of your home! With the company’s mission centered on democratizing AI, you’ll be contributing to creating cutting-edge, open-source tools that shape the future of machine learning. As an intern, you’ll collaborate with talented engineers to enhance the SmolVLM architecture, making it more efficient and performance-driven. Imagine being part of a team that operates on high-performance computing clusters, utilizing powerhouses like the H100s to train models on extensive datasets. You’ll engage in fascinating research while experimenting with state-of-the-art techniques, boosting your skills and understanding of AI. If you’re passionate about making sophisticated technology accessible and are eager to learn and grow, Hugging Face is the perfect environment for you. You won't just be observing; you'll be hands-on with advanced machine-learning techniques and contributing knowledge and innovative ideas as we make high-performance models available for everyone. Get ready to be inspired as you work among some of the sharpest minds in the industry and join a culture that promotes diversity, respect, and community support. We can’t wait to see the fresh perspectives and innovative ideas you bring to our team!

Frequently Asked Questions (FAQs) for ML Research Engineer Internship, Multimodal - US Remote Role at Hugging Face
What is the ML Research Engineer Internship at Hugging Face like?

The ML Research Engineer Internship at Hugging Face provides a unique opportunity to work on the SmolVLM project, focusing on advancing multimodal AI. Interns will engage in developing and optimizing vision-language models, utilize high-performance computing for training, and partake in cutting-edge research. It's a great platform to gain practical exposure in a supportive environment.

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What skills are needed for the ML Research Engineer Internship at Hugging Face?

For the ML Research Engineer Internship at Hugging Face, candidates should have a solid foundation in machine learning, experience with coding, and a passion for open-source development. Familiarity with AI models, especially in multimodal contexts, along with creativity in problem-solving, will also be beneficial.

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What projects will I work on during the ML Research Engineer Internship at Hugging Face?

As an ML Research Engineer Intern at Hugging Face, you’ll work primarily on the SmolVLM project, focusing on optimizing vision-language model architecture, training large datasets, and exploring innovative techniques in multimodal learning. Your contributions will significantly impact the accessibility and efficiency of AI models.

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Is the ML Research Engineer Internship at Hugging Face remote?

Yes, the ML Research Engineer Internship at Hugging Face is fully remote. This allows you to work effectively from anywhere while being part of a global team dedicated to advancing AI technologies and fostering an inclusive community.

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What kind of support does Hugging Face provide to internship candidates?

Hugging Face is committed to supporting its interns. You will receive mentorship from experienced engineers, access to high-performance resources for your projects, and a collaborative environment that values diversity and inclusivity. Your growth and well-being are top priorities.

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How do I apply for the ML Research Engineer Internship at Hugging Face?

To apply for the ML Research Engineer Internship at Hugging Face, visit their careers page, prepare your resume and cover letter, highlighting your skills and interests in open-source AI. Emphasize why you want to work with Hugging Face and any relevant projects you've worked on.

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What can I expect in terms of learning and development from the Hugging Face internship?

During your internship at Hugging Face, you can expect to learn and develop essential skills in AI and machine learning. You will gain hands-on experience with advanced technologies, collaborate with industry experts, and have access to training resources that encourage continuous learning and professional growth.

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Common Interview Questions for ML Research Engineer Internship, Multimodal - US Remote
What interests you about the ML Research Engineer Internship at Hugging Face?

When answering this question, focus on your enthusiasm for open-source AI, your desire to contribute to projects that make advanced technologies accessible, and your excitement about working with the SmolVLM initiative. Share specific aspects of Hugging Face's mission and values that resonate with you.

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Can you explain how vision-language models work?

In your response, articulate the basics of vision-language models, emphasizing their ability to process and understand visual data in conjunction with textual information. Talk about applications such as image captioning and multimodal learning, showcasing your grasp of the topic.

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Describe a machine learning project you've worked on. What were the challenges and results?

Use this opportunity to share a specific project where you faced challenges related to algorithm selection, data preprocessing, or model optimization. Discuss the steps you took to overcome these challenges, what you learned, and the final impact of your work on the project outcome.

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How do you stay updated with the latest developments in AI and machine learning?

Mention various sources such as research papers, online courses, tech blogs, and conferences that you follow to stay current. Highlight any communities or groups you are a part of that discuss advancements in AI, which shows your commitment to continuous learning.

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What is your experience with open-source development?

Discuss any contributions you have made to open-source projects, including your motivation for participating in the community, your understanding of version control systems, and how collaboration works in real-world scenarios. Highlight specific tools and platforms you've used.

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What programming languages and tools are you proficient in for machine learning?

Be sure to list relevant programming languages such as Python and libraries like TensorFlow or PyTorch. Share your proficiency with other tools like Git or Docker that enhance your productivity in machine learning projects.

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How would you approach optimizing a model's performance?

Outline a systematic approach that includes data preprocessing, feature selection, hyperparameter tuning, and model evaluation metrics. Discuss the iterative nature of model optimization and the importance of understanding the problem domain.

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Have you used cloud computing platforms for machine learning? If so, how?

Describe your experience with cloud platforms like AWS, Google Cloud, or Microsoft Azure. Share specific projects where you utilized these services for model training, data storage, or deploying machine learning applications, underscoring the benefits of scalable resources.

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What innovative ideas do you hope to bring to the SmolVLM project?

Consider discussing your passion for exploring new techniques in AI or any specific ideas you have that could enhance model efficiency or user accessibility. Express eagerness to experiment and collaborate with the Hugging Face team.

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How do you handle discrepancies in data when training your models?

Indicate your strategy for addressing data discrepancies, such as data cleaning, augmenting datasets, or utilizing advanced techniques to deal with missing or noisy data. Highlight the importance of ensuring that the training data is representative of real-world scenarios.

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DATE POSTED
November 29, 2024

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