Job Description

Locations

• Beijing, Beijing, China 100045

Salary

Skills Required

• research abilityintermediate
• practical engineering skillsintermediate
• generative AI modelsintermediate
• image restorationintermediate
• video restorationintermediate
• deblurringintermediate
• denoisingintermediate
• super-resolutionintermediate
• inpaintingintermediate
• frame interpolationintermediate
• diffusion modelsintermediate
• GANsintermediate
• transformersintermediate
• building training pipelinesintermediate
• optimizing training pipelinesintermediate
• preprocessingintermediate
• data augmentationintermediate
• distributed trainingintermediate
• evaluating restoration performanceintermediate
• PSNRintermediate
• SSIMintermediate
• LPIPSintermediate
• subjective quality measuresintermediate
• perceptual quality measuresintermediate
• developing inference pipelinesintermediate
• optimizing latencyintermediate
• optimizing throughputintermediate
• optimizing memoryintermediate
• computer visionintermediate
• translating research to solutionsintermediate
• collaborating with cross-functional teamsintermediate
• integrating models into production systemsintermediate

Required Qualifications

• Education: Master’s or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field. (degree in ph)
• Strong background in computer vision and deep learning, with proven experience in generative models (diffusion, GANs, transformers). (experience)
• Proficiency in Python and deep learning frameworks (PyTorch preferred). (experience)
• Experience with large-scale image/video datasets and distributed training (multi-GPU or multi-node). (experience)
• Solid understanding of image and video restoration metrics (PSNR, SSIM, LPIPS) and perceptual evaluation. (experience)
• Hands-on experience with video preprocessing, such as motion estimation and optical flow, frame alignment and stabilization, temporal consistency techniques, and video encoding/decoding. (experience)
• Strong software engineering skills: clean code, Git, debugging, optimization. (experience)

Preferred Qualifications

• Track record of publications or open-source contributions in generative AI, computer vision, or image/video restoration. (experience)
• Experience with real-world video data processing (e.g., raw domain, HDR pipelines, ISP sharpening). (experience)
• Familiarity with cloud-based large-scale dataset management (e.g., S3, distributed file systems). (experience)
• Experience with real-time or near-real-time video restoration and performance optimization. (experience)
• Knowledge of advanced motion analysis (scene change detection, temporal consistency checks, optical flow with RAFT/PWC-Net). (experience)
• Familiarity with deployment on diverse hardware (edge devices, mobile, GPU acceleration). (experience)
• Practical experience with efficient model deployment, including model compression, quantization, distillation, and hardware optimization (e.g. TensorRT, mixed precision). (experience)

Responsibilities

• - 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.

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