You will…

- Conduct fundamental and applied research in generative and predictive world-modeling

• Video generation and prediction.

• Latent diffusion / autoregressive / flow-matching models.

• Multimodal foundation models for driving scenes.

• LLM / VLM / VLA methods for scene understanding, reasoning, and control.

• Generative scenario modeling and controllable simulation.

• Model distillation.

- Collaborate with engineers to integrate models into large-scale, distributed training and rendering pipelines.

- Publish high-impact research at top conferences (CVPR, ECCV, ICCV, NeurIPS, ICLR, ICRA, SIGGRAPH).

- Mentor junior scientists and interns; foster a culture of scientific rigor and rapid experimentation.

- Stay on top of emerging advances in generative AI, differentiable rendering, knowledge distillation/compression, and robotics.

Qualifications:

- Demonstrated technical innovation: You have a Ph.D. in Computer Vision, Machine Learning, Robotics, or a related field or equivalent research experience pushing the boundaries of a technical field..

- Strong prototyping and implementation: You have expert-level Python & PyTorch (or JAX) skills; strong software-engineering fundamentals and experience with distributed training.

- Expert domain knowledge: You have built generative or predictive models of the physical world with scale and efficiency in mind for real-world applications

- Team player: You have worked in a close-knit team of researchers and engineers and have strong communication to deliver successful projects.

Bonus:

- Proven ability to translate research into production-quality code and measurable product impact.

- Demonstrated publications (first-author) in top-tier venues on topics such as world models, generative simulation, video prediction, diffusion, flow-matching, or foundation models for autonomy.