Job Description

Roles & Responsibilities

The Role

As a Research Scientist with the World Model Team, you'll help drive the development of PAN (Physical, Agentic, and Networked) world models next-generation foundation models designed to push machine intelligence beyond language and into the realm of embodied, contextual reasoning. You'll tackle core technical challenges in world modeling and collaborate closely with a multidisciplinary team of researchers and engineers. We are looking for passionate individuals who share our vision and are eager to push the boundaries of AI together.

Key Responsibilities

Develop the foundational world model to accurately simulate the physical world.
Collaborate with engineering and data teams to tackle key challenges in training the world model on large-scale clusters.
Develop metrics and evaluation benchmarks to better assess model performance.
Design and implement a scalable and efficient data annotation pipeline to ensure high-quality labeled data for training and evaluation.
Optimize inference efficiency to enable real-time interaction.

Areas of Focus

Scalable Training Systems: Develop and optimize infrastructure for training multimodal LLMs and video diffusion models at massive scale.
Efficient Data Pipelines: Build scalable video data pipelines and annotation frameworks to support high-quality training data.
Inference Optimization: Enhance inference efficiency through optimization and distillation techniques to enable real-time interaction.
Visual Tokenization: Develop methods for discretizing visual features into tokens for improved model representation.
Quantitative Evaluation: Establish rigorous benchmarks to assess physical accuracy, controllability, and intelligence.
Scaling Laws for Video Pretraining: Investigate scaling law principles to guide efficient video pre-training strategies.

Academic Qualifications

MSc or PhD in Machine Learning or Computer Science, or equivalent industry experience.

Professional Experience

Experience in large-scale model training (LLMs or Diffusion Models) on large clusters.
Hands-on experience with state-of-the-art video generative models (e.g., Sora, Veo2, MovieGen, CogVideoX, etc.).
Experiences in building and optimizing large-scale video data pipelines.
Experience in accelerating diffusion model inference for improved efficiency.
Exceptional problem-solving and troubleshooting skills to tackle complex technical challenges.
Strong systems and engineering expertise in deep learning frameworks such as PyTorch.
Strong communication and collaboration skills for effective cross-functional teamwork.
Ability to navigate ambiguity and drive projects in rapidly evolving research areas.
Research contributions to top-tier conferences or journals (e.g., ICML, ICLR, NeurIPS, ACL, CVPR, COLM, etc.), with published work in relevant domains.

Research Scientist - World Modeling
Institute Of Foundation Models

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