PhD Researcher Intern, World Foundation Models

Responsibilities

• Research and develop model architectures, training objectives, and learning algorithms for video generation and world modeling, including diffusion or flow-matching approaches, multimodal conditioning, and spatial and temporal representations.
• Design controlled experiments and ablation studies, combining quantitative benchmarks with qualitative evaluation to diagnose model behavior, identify failure modes, and validate improvements.
• Explore training and inference efficiency through architectural changes, distillation, model compression, or other optimization techniques.
• Develop data curation and evaluation workflows, including model-assisted filtering, annotation, and quality assessment, and measure their impact on model performance.
• Throughout your project, you will collaborate with research and engineering teams and share findings through reproducible implementations, technical reports, and presentations.
• What we need to see
• Currently pursuing a PhD in Computer Science, Computer Graphics, Computer Engineering, Robotics, or a closely related field.
• Research experience in one or more relevant areas, such as generative modeling, computer vision, video understanding or generation, multi-modal learning, 3D vision, or robotics.
• Strong proficiency in Python and PyTorch, demonstrated through research implementations or substantial projects.
• Hands-on experience implementing, training or fine-tuning, and evaluating deep learning models.

Languages