Define and lead research initiatives in LLM / VLM pre-training and multimodal generation, informed
Design and run large-scale training experiments, analyze results, and improve model capabilities
Explore model architectures, training methods, post-training and reinforcement learning algorithms
Collaborate across teams to bring models into production and share findings through technical

🎯 Requirements

Core pre-training expertise gained on teams such as Qwen, Kimi, GLM, or DeepSeek.
Experience with autoregressive or diffusion models for text-to-image, video, or 3D generation.
Deep expertise in multimodal architectures, pre-training data, reinforcement learning, or synthetic

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