Research and improve open-source video and multimodal video generation foundation models
Focus on one or more areas such as pre-training, supervised fine-tuning, post-training, inference, architecture design, or evaluation
Benchmark models against current state-of-the-art, identify bottlenecks, and propose novel improvements
Work with large-scale video datasets and distributed training systems
Collaborate with researchers and engineers on projects with clear research and publication potential

MSc or PhD candidate in Computer Science, Machine Learning, Computer Vision, or a related technical field
Research topic or experience in image generation, video generation, or multimodal learning
Awareness of open-source video foundation models and their current limitations
Proficiency with PyTorch and modern deep learning workflows
Strong analytical thinking, creativity, and collaboration skills
Prior first-author related publications in CVPR, ICCV, ECCV, NeurIPS, or ICLR
Demonstrated related work, such as research codebase or benchmarks released on GitHub or similar platforms
Experience with large-scale or distributed training
Hands-on experience with diffusion-based, transformer-based, or hybrid video generation models.

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AI Video Research Engineer Intern

Key skills