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Generative AI Engineer, Image/Video Restoration
AppleBeijing, Beijing, China
Apply We are seeking a highly skilled Generative AI Engineer to join our team and drive innovation in image and video restoration. You will work on state-of-the-art algorithms for deblurring, denoising, super-resolution, and related tasks, leveraging generative models such as diffusion models, GANs, and transformers. This role requires both strong research ability and practical engineering skills to develop scalable solutions for real-world applications.
- Research, design, and implement generative AI models for image and video restoration (e.g., deblurring, denoising, super-resolution, inpainting, frame interpolation).
- Build and optimize training pipelines for large-scale datasets, including preprocessing, augmentation, and distributed training.
- Evaluate restoration performance using both objective metrics (PSNR, SSIM, LPIPS) and subjective/perceptual quality measures.
- Develop scalable and efficient inference pipelines, optimizing for latency, throughput, and memory.
- Stay current with the latest research in computer vision and generative AI, and translate novel ideas into practical solutions.
- Collaborate with cross-functional teams to integrate restoration models into production systems.
- Master’s or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.
- Strong background in computer vision and deep learning, with proven experience in generative models (diffusion, GANs, transformers).
- Proficiency in Python and deep learning frameworks (PyTorch preferred).
- Experience with large-scale image/video datasets and distributed training (multi-GPU or multi-node).
- Solid understanding of image and video restoration metrics (PSNR, SSIM, LPIPS) and perceptual evaluation.
- Hands-on experience with video preprocessing, such as motion estimation and optical flow, frame alignment and stabilization, temporal consistency techniques, and video encoding/decoding.
- Strong software engineering skills: clean code, Git, debugging, optimization.
- Track record of publications or open-source contributions in generative AI, computer vision, or image/video restoration.
- Experience with real-world video data processing (e.g., raw domain, HDR pipelines, ISP sharpening).
- Familiarity with cloud-based large-scale dataset management (e.g., S3, distributed file systems).
- Experience with real-time or near-real-time video restoration and performance optimization.
- Knowledge of advanced motion analysis (scene change detection, temporal consistency checks, optical flow with RAFT/PWC-Net).
- Familiarity with deployment on diverse hardware (edge devices, mobile, GPU acceleration).
- Practical experience with efficient model deployment, including model compression, quantization, distillation, and hardware optimization (e.g. TensorRT, mixed precision).
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