Lead of Physical AI Agent, Research Scientist

About the job

• KRAFTON AI is looking for an exceptional Research Scientist to help build the next-generation humanoid robot foundation models and embodied AI systems. Our mission is to develop general-purpose embodied agents that can learn, adapt, and master complex skills across both virtual and real-world environments.
• You’ll be joining a highly collaborative and forward-thinking research team, known for impactful work in multimodal foundation models, large-scale robot learning, game AI, and physical simulation. In this role, you’ll have the opportunity to drive ambitious new research projects and contribute meaningfully to our long-term vision and product roadmaps.
• KRAFTON Physical AI Team combines KRAFTON’s AI expertise with technologies that understand physical environments to research and develop Embodied AI - AI that can interact within the real world. Leveraging our experience with Co-Playable Character (CPC) and Reinforcement Learning (RL) technologies built through games, we create AI agents that deliver new kinds of user experiences. Ultimately, our goal is to expand KRAFTON’s AI research into the physical world and explore how AI can naturally collaborate and interact with humans.

Main Responsibilities 

• Design and develop cutting-edge AI algorithms and models for general-purpose humanoid robots and embodied agents.
• Research and optimize behavior and interaction models of AI agents tailored for physical environments, using techniques such as Imitation Learning, Reinforcement Learning (RL), Human-Robot Interaction (HRI), and Sim-to-Real Transfer.
• Build scalable training and inference pipelines for large foundation models customized for embodied intelligence.
• Develop learning environments that enable AI agents to adapt and respond autonomously in real-world spaces, leveraging KRAFTON’s AI technologies and in-game interaction experience.
• Optimize and deploy AI models in both high-fidelity simulation environments and on real-world robotic platforms.
• Explore new modes of interaction that bridge the virtual and physical worlds, applying research outcomes to real products and services.
• Collaborate closely with cross-functional research and engineering teams across KRAFTON AI to bring innovative Sim-to-Real Transfer research into practical applications.

Key Qualifications

• Ph.D. in Artificial Intelligence, Computer Science, Electrical Engineering, or a related field; or equivalent hands-on research experience.
• Bilingual in English & Korean
• Strong collaborative mindset with leadership skills to align and motivate cross-functional teams.
• Strong engineering background with proven skills in rapid prototyping and working with modern model training frameworks (e.g., PyTorch, JAX, TensorFlow). Proficiency in Python or C++ is required
• Experience in developing learning-based algorithms for manipulation, locomotion, and navigation using techniques such as RL and imitation learning.
• Prior experience in one or more of the following domains is highly valued:
  • Multimodal Foundation Models
  • Deep understanding and hands-on experience in one or more of the following areas: LLMs; Large vision-language models; Vision-Language Models and Vision-Language-Action models.
  • Proven ability to work with large-scale AI/ML systems and distributed computing infrastructure.
  • Robotics
  • Experience in key areas such as foundation and diffusion models; Agent-based Learning, Sim-to-Real Transfer, Human-Robot Interaction. and Interactive Imitation Learning.
  • Deep understanding of robot kinematics, dynamics, and sensor integration.
  • Ability to safely operate robot hardware, lab equipment, and tools.
  • Knowledge of control methods, including PID, model predictive control, and whole-body control.
  • Practical knowledge of robot hardware design, prototyping, and hands-on assembly.

Preferred Qualifications

• Publication record in top-tier machine learning and robotics conferences such as NeurIPS, ICML, ICLR, ICRA, IROS, RSS, or CoRL.
• Hands-on experience with simulation platforms like IsaacSim and Mujoco, as well as robot systems based on ROS, with a proven track record of deploying end-to-end learning models.
• Strong skills in analyzing and utilizing large-scale, high-dimensional data, particularly in the context of training and evaluating deep learning models.
• Ability to collaborate effectively with cross-functional teams, bridging the gap between AI research and interactive content, and leveraging end-to-end learning pipelines to enhance AI agent behavior and interaction.

Location

• Seoul, South Korea (Yeoksam, Center Field) 

Employment Type

• Full-time

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