ML Research Engineer Internship, SmolLMs pretraining and datasets - EMEA Remote

Here at Hugging Face, we’re on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better.

We have built the fastest-growing, open-source, library of pre-trained models in the world. With more than 500K+ models and 250K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Algolia, and Grammarly.

About the Role

Smol models are an exciting area of research as they enable cheaper inference and can be run on-device allowing for more customization and ensuring privacy. The SmolLM team at Hugging Face is pushing the frontier of smol models by building high quality pre-training and post-training datasets [1,2], and applying the latest architecture and training techniques to develop state-of-the-art models [2,3]. The dataset processing can leverage our scalable CPU cluster and the models are trained on a state-of-the-art H100 cluster with close to 100 nodes.

In this internship you will work alongside the SmolLM team and work towards building the next generation of smol language models by iterating on datasets and models quickly and finally training models on our distributed training infrastructure. If you are passionate about training LLMs and building high-quality datasets, proficient in Python, we would love to hear from you! Join the SmolLM team and collaborate on developing the best smol models in the field. Checkout hf.co/science for more information about the science team at Hugging Face, and hf.co/HuggingFaceTB for more information on the SmolLM projects.

[1] The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale https://arxiv.org/abs/2406.17557

[2] SmolLM - blazingly fast and remarkably powerful https://huggingface.co/blog/smollm

[3] SmolLM2 https://github.com/huggingface/smollm

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.

Requirements

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.