Sr. Physical AI Research Scientist

Job summary

Design and develop safe, robust robot agents that can perceive, reason, and act in real-world environments. This includes building safety architectures, scaling via simulation, and implementing robotic foundation models.

Job details

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. As a global leader in consumer electronics, LG is committed not only to enriching the lives of our customers, but also to create rewarding experiences for our employees. We offer meaningful challenges, continuous learning opportunities, and a workplace culture that recognizes collaboration and rewards excellence. 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). 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 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 Preferred Technical Skills 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 MuJoCo Research and Collaboration Ability to identify, formulate, and define novel research problems Experience of collaborating with academic and industry partners Proven track record of translating research concepts into production-grade prototypes and deployed systems Strong commitment to mentoring junior researchers and fostering a high-performing R&D culture Personal Attributes Highly creative problem-solver with a demonstrated ability to develop novel, real-world solutions Comfortable operating in fast-paced, technically complex, and highly collaborative environments Open to feedback from senior colleagues across the organization and actively incorporates input into work Strong communication skills, with the ability to clearly articulate complex research ideas to both technical and non-technical stakeholders Note: This posting is for an existing vacancy. The expected base salary range for this position is $140k - $180k. Actual total compensation may include variable incentive pay. The determination of an applicant's base salary is based on the applicant's skills, competencies, location, and unique qualifications. Artificial intelligence will be used in sourcing, reviewing and communicating with candidates for this position. This job description is not intended to be all-inclusive. Employee may perform other related duties as negotiated to meet the ongoing needs of the organization. The organization offers an attractive compensation package that encompasses a competitive salary and excellent benefits. Conditions of Employment: It is the candidate’s sole responsibility to obtain any work permits/visas or other authorizations which may be required to legally work in Canada prior to commencing employment.

What you’ll do

Design and develop safe, robust robot agents that can perceive, reason, and act in real-world environments. This includes building safety architectures, scaling via simulation, and implementing robotic foundation models.

Requirements

Requires a PhD in ML/AI/Robotics with 3+ years of experience or an MSc with 6+ years of experience. Candidates must have a strong publication record in top-tier conferences and deep expertise in GenAI and robotic manipulation.

Benefits

• Competitive Salary • Excellent Benefits • Variable Incentive Pay

Listed skills

• Machine learning · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Robotics
• Reinforcement Learning
• Computer Vision
• PyTorch
• JAX
• GenAI Foundation Models
• Sim-to-Real Transfer
• ROS1/ROS2
• Isaac Sim
• MuJoCo
• Imitation Learning
• Multimodal Learning
• NLP
• Constitutional AI
• Robot Safety Architecture

Job areas

• Science & Research
• Technology
• Engineering
• Software
• Manufacturing