Generative AI Research Intern - Image-to-Video Diffusion

Apple is hiring a Generative AI Research Intern - Image-to-Video Diffusion in Beijing. When: Summer 2026. Posted November 2, 2026. Checked against the employer's posting on November 12, 2026.

Summary We are seeking a talented Generative AI Research Intern to work on image-to-video (I2V) diffusion models. This role focuses on building next-generation generative systems that transform still images into high-quality, temporally consistent videos. You will work closely with researchers and engineers to explore cutting-edge diffusion and transformer-based architectures for video generation, motion modeling, and temporal representation learning.

Description You will: - Lead experimental studies on image-to-video diffusion techniques and temporal generative modeling. - Design, implement, and evaluate model variants for motion modeling and temporal consistency. - Build experimental pipelines and run ablation studies and benchmarking experiments. - Analyze results and summarize findings in internal research reports or presentations. - Study and reproduce recent work in video diffusion and I2V generation. - Participate in research discussions and proposal of new ideas.

Minimum qualifications • Currently pursuing a Master’s or Ph.D. degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related field. • Strong background in computer vision and deep learning, with research or project experience in diffusion models, flow-matching, or transformer-based generative models applied to video generation or spatiotemporal tasks. • Experience or solid understanding of image-to-video models, video generation, temporal modeling, or video representation. • Proficiency in Python and PyTorch, with the ability to implement research code independently. • Familiarity with model training workflows, ablation studies, and experimental result analysis. • Familiarity with generative video evaluation metrics such as LPIPS, FVD, and perceptual assessment methods. • Strong motivation for independent research and the ability to drive projects with minimal supervision. • Ability to read, reproduce, and critically analyze recent research papers.

Preferred qualifications • Understanding of video fundamentals such as motion estimation, optical flow, temporal consistency, or alignment techniques. • Prior experience with large-scale training, multimodal models, or video datasets. • Publications in top-tier conferences or journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, etc.). • Strong curiosity and motivation for independent research in generative video modeling.