Sr. Physical AI Research Scientist

POSITION SUMMARY

LG Toronto AI Lab is looking for a Sr. Physical AI Research Scientist who will contribute to designing and developing robot agents that operate safely and robustly in the real world. This role focuses on advancing physical intelligence agents that perceive, reason, and act under real-world constraints, while explicitly addressing safety, uncertainty, and reliability, which are key priorities for LG Electronics as a leading global corporation in the design and development of robotics systems.

Our mission is to develop safe robotic agents that robustly align their actions with high-level intent, environmental constraints, and safety requirements, even under uncertainty in open-world settings. In this role, you will contribute to:

• Robot Safety Architecture:
  Design and implement safety verification and monitoring modules within the robot agent architecture
• Safety and Generalization:
  Develop methods to improve the safety, robustness, and generalization of robotic foundation models, including vision-language-action (VLA) models and world models
• Scaling via Simulation:
  Build and maintain large-scale simulation frameworks leveraging generative models and domain randomization to enable scalable robot learning
• Continual Learning:
  Develop safe exploration and continual learning approaches that enable physical agents to adapt to unstructured environments while respecting strict safety constraints
• Simulation-to-Reality Transfer:
  Develop scalable, safety-critical simulation pipelines to rigorously evaluate and stress-test robot agents prior to real-world deployment

• Quickly turn research concepts into practical, validated implementations
• Contribute to data collection strategies and training pipelines
• Develop imitation learning, reinforcement learning, and/or multimodal learning algorithms
• Fine‑tune robotic foundational models, including but not limited to VLMs, VLAs and world models
• Design safety-aware robotic manipulation solutions via fine‑tuning, policy steering, and Best‑of‑N sampling
• Develop methods for Sim‑to‑real transfer, data‑efficient learning (offline RL, self‑supervised learning), robustness to noise and distribution shift
• Collaborate with Physical Intelligence, Embodied AI, and Robotics teams across the organization to develop prototypes and deploy models on physical robots
• Support and contribute to R&D collaborations with academic and industry partners
• Mentor junior team members and contribute to research direction
• Additional duties as assigned

• PhD in Machine Learning, AI, Robotics, or related fields with 3+ years of post‑graduate R&D experience, OR
• M.Sc. in ML/AI/Robotics with 6+ years of experience
• Strong publication record in top‑tier conferences or journals (NeurIPS, ICLR, CVPR, CoRL, ICRA) related to Computer Vision, Continual Learning, Reinforcement Learning, and Robotics
• General Technical Skills
• Strong foundation in natural language processing (NLP), computer vision, reinforcement learning (RL), and robotics
• Deep expertise in GenAI foundation models, including but not limited to multimodal large language models (MLLMs), vision‑language models (VLMs), diffusion and flow matching models
• Hands‑on experience with constitutional AI, alignment, optimization, and reward modeling/shaping
• Knowledge of transfer learning, meta learning, and contrastive learning is a plus
• Proven ability to design, execute, and rigorously evaluate experiments in complex ML/AI systems
• Strong proficiency in PyTorch and/or JAX

• Experience with robotic manipulation and navigation tasks in simulation and/or real‑world settings
• Expertise in robotic foundation models, including diffusion policies, VLAs, and (latent) world models
• Pre‑training and post‑training of robotic foundation models are second nature to you
• Familiarity with ROS1 and/or ROS2 for robot software development, system integration, and deployment pipelines is a plus
• Practical experience with simulation platforms such as Isaac Sim, Genesis, and Mu Jo Co
• Research and Collaboration
• Ability to identify, formulate, and define novel research problems
• Experience of collaborating with academic and…