Conduct research and develop advanced Embodied AI methods for robotic perception, reasoning, and control, with emphasis on:

World Models for action-conditioned prediction, planning, and long-horizon decision-making

Vision-Language-Action (VLA) models for general-purpose robotic manipulation

Post-training methods such as supervised fine-tuning, preference optimization, policy improvement, and online/offline adaptation

Reinforcement Learning for improving robustness, generalization, and task performance

Design and execute large-scale experiments to advance robot learning capabilities across challenging manipulation and embodied reasoning tasks.

Collaborate closely with robotics, hardware, and infrastructure teams to bring research ideas into real robotic systems.

Evaluate new methods on real-world and benchmark tasks, and help define rigorous research standards for the team.

Contribute to technical reports, open research discussions, and publications at leading conferences where appropriate.

Currently pursuing or recently completed a PhD in Computer Science, Robotics, Machine Learning, or a related field.

Strong research background in Embodied AI, robot learning, foundation models, or a closely related area.

Hands-on experience with one or more of the following:

World models

Vision-Language-Action (VLA) models

Post-training / policy fine-tuning

Reinforcement learning , including offline RL, online RL, or RL for control

Strong understanding of modern machine learning architectures, including transformers, diffusion models, and multimodal learning systems.

Proficiency with deep learning frameworks such as PyTorch, JAX, or TensorFlow.

Strong experimental and problem-solving skills, with the ability to independently drive research ideas from concept to evaluation.

Requires 5 days/week in-office collaboration with the team.