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Generative AI Research Intern - Image-to-Video Diffusion

Apple6,343 open roles

Where
Beijing, China
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Your applicationOpen nowGenerative AI Research Intern - Image-to-Video DiffusionApple · Beijing, China

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market. Apple postings stay open a median of 41 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.8%30 days
  6. 59.0%60 days
This job: posted today

Apple median: 41 days open

The posting

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.

EEO Statement

Apple is an equal opportunity employer that is committed to inclusion and diversity, and thus we treat all applicants fairly and equally. Apple is committed to working with and providing reasonable accommodation to applicants with physical and mental disabilities.

Accessibility

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

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