Senior Interactive Vision Model Researcher

Design and iterate on the next-generation video generation foundation architecture.

Explore core technologies for long video generation, including long-context attention, KV Cache compression, and memory mechanisms.

Research high-compression-ratio video tokenizers and advance unified modeling capabilities for multi-resolution and multi-frame-rate videos.

Lead video pre-training at large token scale, defining data mixture and curriculum learning strategies.

Explore Scaling Laws, define scientific scale-up paths, and continuously improve key model capabilities.

Track industry state-of-the-art , lead comparative experiments, drive technical roadmap decisions, and publish academic papers.

Ph.D. in AI-related fields with first-author papers at top-tier conferences.

Proficient in the principles and engineering implementation of diffusion models and autoregressive generation.

Experience training video/image generation models from scratch; highly proficient in PyTorch and large-scale distributed training.

Deep understanding of the design trade-offs in Video VAE/Tokenizers, with 3+ years of relevant research experience.

Publications related to video generation or diffusion models.

Experience in core industry product R&D or hands-on experience with the latest technologies is preferred.

Experience leading end-to-end video foundation model projects is preferred.