Master's degree or PhD in Computer Science, Machine Learning, Electrical Engineering, Robotics, Applied Mathematics, or equivalent experience.
Strong programming skills in Python with practical experience in PyTorch.
Hands-on experience in world modeling, video generation, spatiotemporal prediction, or learned simulation.
Solid foundation in deep learning and generative or predictive modeling.
Experience with large-scale model training or inference and distributed machine-learning systems.
Ability to design rigorous experiments and analyze results effectively.
Strong software-engineering practices and collaborative problem-solving skills.
Responsibilities
Research, develop, and adapt models for world generation and future prediction.
Advance model capabilities in controllability and realistic world dynamics.
Develop scalable and efficient training and inference systems for world models.
Improve data and evaluation methods to enhance model quality.
Design experiments and analyze model behavior to guide further research.
Collaborate across teams to implement new ideas and share technical findings.
Benefits
Collaborative work environment with cross-disciplinary teams.
Opportunities for hands-on research and development.
Access to cutting-edge technologies and methodologies.
Support for professional growth and continuous learning.
Full Job Description
We are seeking a hands-on [Research Engineer/Research Scientist] with expertise in world modeling, video generation, predictive modeling, or learned simulation. You will develop models that generate environments and future observations, capture temporal dynamics, and respond to actions or interventions.
You will own research problems from hypothesis and implementation through training, evaluation, and failure analysis. Working with colleagues across modeling, data, evaluation, and infrastructure, you will translate research ideas into working models and measurable improvements. Key Responsibilities
Research, develop, and adapt models for world generation, future prediction, and simulation.
Advance model capabilities in controllability, consistency, and realistic world dynamics.
Develop scalable and efficient training and inference systems for world models.
Improve data and evaluation methods to support model quality and generalization.
Design experiments, analyze model behavior, and use findings to guide further research.
Collaborate across research and engineering to turn new ideas into reliable implementations and share technical findings.
Requirements
Master's degree or PhD in Computer Science, Machine Learning, Electrical Engineering, Robotics, Applied Mathematics, or a related field, or equivalent practical experience.
Strong programming skills in Python and practical experience with PyTorch.
Hands-on experience developing and training models in one or more of world modeling, video generation, spatiotemporal prediction, or learned simulation.
A solid foundation in deep learning and generative or predictive modeling, with the ability to implement and evaluate research ideas.
Experience with large-scale model training or inference and distributed machine-learning systems.
Ability to design rigorous experiments, analyze results, and draw sound technical conclusions.
Strong software-engineering practices, clear technical communication, and collaborative problem-solving skills.
Preferred Qualifications
Experience in one or more of the following areas would be an advantage:
Research in controllable generation, long-horizon prediction, or interactive simulation.
Fine-tuning, adaptation, or extension of advanced pretrained video generation and world models, including Cosmos, Wan, LingBot, or comparable models.
Methods for improving model quality, efficiency, or scalability, including adaptation and distillation.
Research contributions demonstrated through publications, open-source work, or industry projects.
Application experience in robotics, embodied AI, autonomous driving, or other simulation domains.
