At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders.
As an Open-Source ML engineer in Computer Vision, you will work mainly with existing open-source libraries, such as Transformers and Datasets to boost the support for vision or multi-modal models and datasets. You will bring your computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem and work with us to provide the best, simplest, and most intuitive computer-vision library in the industry.
Responsibilities:
- Work with existing open-source libraries to boost support for vision or multi-modal models and datasets.
- Bring computer vision expertise to provide the best computer-vision tool stack in the machine learning ecosystem.
- Collaborate with researchers, ML practitioners, and data scientists on a daily basis.
- Foster one of the most active machine learning communities, helping users contribute to and use the tools that you build.
Requirements:
- Deep expertise in computer vision: object detection, segmentation, generative models, or multimodal systems.
- Strong open-source presence: You’ve contributed significantly to CV libraries (e.g., OpenCV, Detectron2, MMDetection, or Hugging Face’s own transformers/diffusers), as a Core-Contributor or maintainer.
- Scalability mindset: Experience optimizing models for production, deploying at scale, or improving inference efficiency.
- Collaboration & mentorship: You enjoy working with cross-functional teams, reviewing PRs, and guiding junior contributors.
- Alignment with our mission: You believe in democratizing AI and want to empower millions of builders with state-of-the-art tools.
If you love open-source, are passionate about the new development of Transformers models in computer vision, have experience building, optimizing, and training such models in PyTorch and/or TensorFlow, serving them in production, and want to contribute to one of the fastest-growing ML libraries, 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 backgrounds complement one another. We're happy to consider where you might be able to make the biggest impact.
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