AI Research Scientist Intern (PhD), Embodied AI

We are seeking an AI Research Scientist Intern (PhD) to join us in advancing the frontier of Embodied AI for robotics. This role is centered on developing next-generation robot intelligence, with a particular focus on world models, Vision-Language-Action (VLA) models, post-training, and reinforcement learning.

You will work alongside a team of world-class researchers and engineers on ambitious, real-world problems at the intersection of foundation models, decision-making, and robotics. This is an opportunity to help shape core research directions, build cutting-edge systems, and contribute to work with strong potential for publication at top-tier conferences.

Responsibilities

• 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.

Qualifications

• 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.

Preferred Skills

• Experience with robotic manipulation, imitation learning, or large-scale robot policy learning.
• Familiarity with post-training pipelines for large models, including supervised fine-tuning, preference optimization, or RL-based policy improvement.
• Experience with simulation-to-real transfer, long-horizon decision-making, or action-conditioned prediction.
• Exposure to multimodal learning across vision, language, action, and proprioception.
• Background in bimanual manipulation, synthetic data generation, or 3D/spatial reasoning for robotics.

Why Join Us

• Work on challenging, high-impact problems at the frontier of AI and robotics
• Collaborate with a highly technical, research-driven team
• Gain hands-on experience building real systems for Embodied AI
• Opportunity to contribute to research publications and help shape emerging directions in the field
• See your work influence both long-term research and practical robotic capabilities