Responsibilities
1.Design and iterate on the next-generation video generation foundation architecture.
2.Explore core technologies for long video generation, including long-context attention, KV Cache compression, and memory mechanisms.
3.Research high-compression-ratio video tokenizers and advance unified modeling capabilities for multi-resolution and multi-frame-rate videos.
4.Lead video pre-training at the 10-100 billion token scale, defining data mixture and curriculum learning strategies.
5.Explore Scaling Laws, define scientific scale-up paths, and continuously improve key model capabilities.6.Track industry state-of-the-art , lead comparative experiments, drive technical roadmap decisions, and publish academic papers.
Requirements
1.Ph.D. in AI-related fields with first-author papers at top-tier conferences.
2.Proficient in the principles and engineering implementation of diffusion models and autoregressive generation.
3.Experience training video/image generation models from scratch; highly proficient in PyTorch and large-scale distributed training.
4.Deep understanding of the design trade-offs in Video VAE/Tokenizers, with 3+ years of relevant research experience.
5.Publications related to video generation or diffusion models.
6.Experience in core industry product R&D or hands-on experience with the latest technologies is preferred.
7.Experience leading end-to-end video foundation model projects is preferred.
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