Sr. Physical AI Research Scientist

Position Type: Full-Time, Permanent

Department: Toronto AI Lab

Work Location: Downtown Toronto

Work Arrangement: Hybrid: 3 Days per Week On-Site

At LG, we create Innovation for a Better Life.

We design products and services that make life better, easier, and more enjoyable. Whether it’s through smart functionality, design, or innovative technology, our home appliances, including kitchen, laundry and lifestyle solutions, media entertainment products including televisions, sound bars, projectors, computer monitors and laptops, to cutting-edge business solutions in IT, digital signage, and HVAC, we’re driven by one simple idea: to make Life Good.

Across our global network, LG employees share a common mission: to bring the Life’s Good promise to our customers by creating a better life for people, and a better future for our planet through our smart life solutions. Our relentless drive for innovation, combined with our culture of challenge and change, enables us to lead in today’s dynamic market.

At LG, Life’s Good.

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 (from industrial and home robots to humanoids).

Requirements 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

You will collaborate with researchers and engineers across multiple teams within the organization to build agents that are not only capable, but also safe, reliable, and robust in real-world environments.

Principal Responsibilities

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 solution via including but not limited to 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

Knowledge, Skills, And Abilities

Education and Professional Experience

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 in top-tier conferences or journals (incl., 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