Multimodal Research 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.

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...

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 1 Million+ models and 320K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA.

About the Role

We are in a “GPT-3 moment” for multimodal models: vision and language models are an order of magnitude more accurate and versatile than what they were 18 months ago, and yet still make relatively frequent mistakes. Meanwhile, having strong open-source and open-access alternatives to close-source models is critical for the healthiness and sustainability of the AI community, including AI research.

In this role, you will contribute to...