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Implement AI models for 3D data generation using deep learning techniques. Strong experience in 3D computer vision and 3D graphics required. Responsibilities include data preprocessing, feature engineering. Expert in GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).





What You’ll Do

  • Developing and implementing deep learning models for 3D data, such as point clouds, voxel grids, and triangle meshes.

  • Training and fine-tuning deep learning models on large datasets of 3D data, such as 3D scans of real-world objects and environments.

  • Using deep learning to extract features and representations from 3D data, such as object segmentation, surface normal estimation, and 3D object recognition.

  • Researching and implementing new techniques for 3D deep learning, such as point cloud convolutional neural networks and volumetric convolutional neural networks.

  • Collaborating with other engineers and researchers to integrate 3D deep learning models into other systems, such as robotics and autonomous vehicles.

  • Building and deploying 3D deep learning models on various platforms, including cloud, edge, and mobile devices.

  • Optimizing the performance of 3D deep learning models, including reducing memory and computational requirements and improving inference speed.

  • Communicating the results of their research and development activities to stakeholders and customers, including technical and non-technical audiences.

  • Keeping up with current research and developments in the field of 3D deep learning and identifying new opportunities for applying 3D deep learning to real-world problems.

  • Utilizing NVIDIA's deep learning frameworks, such as CUDA, cuDNN, and TensorRT, to optimize the performance of 3D deep learning models.

  • Using NVIDIA's Jetson platform for deploying deep learning models on edge devices.

  • Using NVIDIA's GPU-accelerated cloud platforms, such as NVIDIA GPU Cloud (NGC), for training and deploying deep learning models in the cloud.

  • Leveraging NVIDIA's AI-specific hardware, such as the NVIDIA A100 Tensor Core GPU, for faster and more efficient training and inference of deep learning models.

  • Utilizing NVIDIA's AI development tools, such as DeepStream and Isaac, to develop and deploy AI-powered applications for robotics and autonomous systems.

  • Using NVIDIA's Clara platform for medical imaging and other 3D data analysis.

  • Utilizing NVIDIA's Omniverse platform for creating and training models in virtual environments.

Who You are

  • Strong technical skills: A 3D Deep Learning Engineer should have a solid understanding of deep learning and computer vision, as well as experience with programming languages such as Python and C++.

  • Experience with 3D data: You should have experience working with 3D data, such as point clouds, voxel grids, and triangle meshes, and should be familiar with the techniques and algorithms used for processing 3D data.

  • Experience with deep learning frameworks: You should have experience working with deep learning frameworks, such as TensorFlow, PyTorch, and NVIDIA's CUDA, cuDNN, and TensorRT, and should be familiar with the techniques used to optimize the performance of deep learning models.

  • Strong problem-solving skills: You should have strong problem-solving skills and be able to develop creative solutions to complex technical challenges.

  • Attention to detail: You should be meticulous and pay attention to detail, as small errors or bugs in the code can cause significant problems.

  • Strong communication skills: You should be able to effectively communicate technical ideas and solutions to both technical and non-technical audiences.

  • Flexibility and Adaptability: You should be able to adapt and learn quickly as the field of AI is constantly evolving and new techniques and technologies are emerging.

  • Creativity: You should have a creative mindset and be able to come up with new and innovative solutions to problems.

  • Team Player: You should have good collaboration skills and be able to work well in a team environment.

  • Passion for technology: You should have a genuine passion for technology and a desire to learn and stay current with the latest developments in the field.

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