ML Engineer

About Ultralytics: At Ultralytics https://ultralytics.com/, we commit to relentless innovation in the AI space and seek team members https://www.ultralytics.com/about who resonate with our ambition to produce the world's best YOLO AI models https://ultralytics.com/yolo. If you're obsessed with AI, eager to make an impact on the world, and thrive in dynamic, high-intensity environments, we invite you to apply for a position on our team. 🌎 Where and how you can work This full-time Machine Learning Engineer position is based at our brand-new Ultralytics office https://www.ultralytics.com/about in Shenzhen, China, with a hybrid model of three days in-office and two days remote. Applicants must have legal authorization to work in China, as Ultralytics does not provide visa sponsorship. 🚀 What you'll do As a Machine Learning Engineer at Ultralytics, you will be at the forefront of developing and refining our world-class Ultralytics YOLO https://docs.ultralytics.com/models/yolo26/ models. You will work on the entire lifecycle of our models, from research and development to high-performance deployment. Key responsibilities include: - Developing, training, and validating state-of-the-art models for a variety of computer vision tasks https://docs.ultralytics.com/tasks/, including detection, segmentation, and classification. - Writing highly efficient, scalable, and production-ready code in Python https://docs.ultralytics.com/usage/python/ using the PyTorch https://pytorch.org/ framework. - Optimizing models for high-performance inference on diverse hardware using tools like NVIDIA TensorRT https://docs.ultralytics.com/integrations/tensorrt/, OpenVINO https://docs.ultralytics.com/integrations/openvino/, and ONNX https://docs.ultralytics.com/integrations/onnx/. - Managing and processing large-scale datasets https://docs.ultralytics.com/datasets/ and implementing advanced data augmentation https://docs.ultralytics.com/guides/yolo-data-augmentation/ strategies. - Designing and maintaining robust CI/CD pipelines https://docs.ultralytics.com/help/CI/ with GitHub Actions for automated model training, testing, and benchmarking. - Collaborating with our research and engineering teams to implement cutting-edge techniques and contribute to our open-source repositories https://github.com/ultralytics. - Engaging with our global community by creating documentation, tutorials, and supporting users to solve real-world problems with our technology. Your expertise will be critical in advancing the capabilities of our models and supporting Ultralytics' mission https://www.ultralytics.com/about to make AI easy and accessible for everyone. 🧠 Skills and experience - 5+ years of professional experience in Machine Learning Engineering https://www.ultralytics.com/blog/aspiring-ml-engineer-8-tips-you-need-to-know or a similar role. - Deep expertise in Python https://www.python.org/ and deep learning frameworks, with a strong preference for PyTorch https://pytorch.org/. - Proven experience with computer vision https://www.ultralytics.com/glossary/computer-vision-cv and a strong understanding of model architectures like transformers and CNNs. - Hands-on experience with model optimization https://www.ultralytics.com/glossary/optimization-algorithm (i.e., quantization and pruning) and model deployment https://docs.ultralytics.com/guides/model-deployment-options/ frameworks such as TensorRT https://developer.nvidia.com/tensorrt, ONNX Runtime https://onnxruntime.ai/, and OpenVINO https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html. - Proficiency with CUDA https://developer.nvidia.com/cuda-zone programming and optimizing code for GPU acceleration. - Strong background in MLOps https://www.ultralytics.com/glossary/machine-learning-operations-mlops practices, including CI/CD using GitHub Actions https://github.com/features/actions and containerization with Docker https://www.docker.com/. - Excellent problem-solving skills and the abi…