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