Research Scientist - World Modeling

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    "city": "Sunnyvale",
    "region": "CA",
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  "lists": [
    {
      "text": "Key Responsibilities",
      "content": "\n<li>Develop the foundational world model to accurately simulate the physical world.</li>\n<li>Collaborate with engineering and data teams to tackle key challenges in training the world model on large-scale clusters.</li>\n<li>Develop metrics and evaluation benchmarks to better assess model performance.</li>\n<li>Design and implement a scalable and efficient data annotation pipeline to ensure high-quality labeled data for training and evaluation.</li>\n<li>Optimize inference efficiency to enable real-time interaction.&nbsp;</li>\n"
    },
    {
      "text": "Areas of Focus",
      "content": "\n<li><strong>Scalable Training Systems</strong>: Develop and optimize infrastructure for training multimodal LLMs and video diffusion models at massive scale.&nbsp;</li>\n<li><strong>Efficient Data Pipelines</strong>: Build scalable video data pipelines and annotation frameworks to support high-quality training data.&nbsp;</li>\n<li><strong>Inference Optimization</strong>: Enhance inference efficiency through optimization and distillation techniques to enable real-time interaction.&nbsp;</li>\n<li><strong>Visual Tokenization</strong>: Develop methods for discretizing visual features into tokens for improved model representation.&nbsp;</li>\n<li><strong>Quantitative Evaluation</strong>: Establish rigorous benchmarks to assess physical accuracy, controllability, and intelligence.&nbsp;</li>\n<li><strong>Scaling Laws for Video Pretraining</strong>: Investigate scaling law principles to guide efficient video pre-training strategies.&nbsp;</li>\n"
    },
    {
      "text": "Academic Qualifications",
      "content": "\n<li>MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience.&nbsp;</li>\n"
    },
    {
      "text": "Professional Experience",
      "content": "\n<li>Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.&nbsp;</li>\n<li>Hands-on experience with state-of-the-art video generative models (e.g., Sora, Veo2, MovieGen, CogVideoX, etc.).&nbsp;</li>\n<li>Experiences in building and optimizing large-scale video data pipelines.&nbsp;</li>\n<li>Experience in accelerating diffusion model inference for improved efficiency.&nbsp;</li>\n<li>Exceptional problem-solving and troubleshooting skills to tackle complex technical challenges.&nbsp;</li>\n<li>Strong systems and engineering expertise in deep learning frameworks such as PyTorch.&nbsp;</li>\n<li>Strong communication and collaboration skills for effective cross-functional teamwork.&nbsp;</li>\n<li>Ability to navigate ambiguity and drive projects in rapidly evolving research areas.&nbsp;</li>\n<li>Research contributions to top-tier conferences or journals (e.g., ICML, ICLR, NeurIPS, ACL, CVPR, COLM, etc.), with published work in relevant domains.&nbsp;</li>\n"
    }
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  "country": "US",
  "createdAt": 1746217189466,
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    "team": "Research",
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