Job Opening: AI Robotics Engineer / Physical AI Engineer (Full-time or Internship)

Position Overview

Join us at the forefront of next-generation Physical AI technology, where you will be instrumental in integrating AI-driven perception, reasoning, and action control into physical robots. Utilizing cutting-edge techniques such as Multimodal AI, Reinforcement Learning (RL), Imitation Learning (IL), and advanced simulation technology, you will strive to realize intelligent robots capable of adaptively operating in the real world. This central role involves leading the entire lifecycle—from research and development to implementation and evaluation— directly impacting the robotics strategy and product development of our client companies.

Expected Outcomes

• Develop and implement advanced Physical AI models leveraging multimodal information (e.g., image, audio, text) to enhance robot autonomy and generality.

• Create practical and scalable robotic solutions through the integration of AI models across diverse robot platforms.

• Shorten the development cycle and improve task execution accuracy by seamlessly fusing simulation and real-world validation.

Key Responsibilities (Determined based on expertise)

1. AI Technology Research and Development

2. Design, develop, and evaluate Physical AI models that integrate multimodal inputs (vision, language, tactile, etc.) (e.g., VLA, RL, IL, Diffusion-based Policy).

3. Build high-level planning and decision-making modules applying Vision-Language Models (VLM), Foundation Models, and Transformer-based architectures.

4. Integrate AI models with existing robotics control systems (SLAM, motion planning, force control, etc.) to achieve environmentally adaptive robots.

5. Integration into Physical Robots

6. Implement and evaluate AI models on various platforms, including manipulators, mobile robots, quadruped robots, and humanoids.

7. Drive model performance optimization through continuous learning and online adaptation experiments using physical hardware.

8. Collaborate with the hardware team to optimize interfaces with sensors and control systems.

9. Simulation Environment Construction and Utilization

10. Construct highly realistic verification environments for physical tasks using tools like Isaac Sim, Gazebo, MuJoCo, and NVIDIA Omniverse.

11. Research and apply Sim2Real techniques to minimize the gap between simulation and the real world.
12. Design data generation pipelines and manage efficient dataset construction and management.

Minimum Qualifications

Location Requirement

• Must be currently residing in
  Japan
  or have a clear intention and eligibility to relocate to
  Japan
  .

Education & Experience

• Bachelor's degree or equivalent knowledge/skills in Computer Science, Robotics, Machine Learning, or a related field.
• Student interns (undergraduate, master's, or Ph.D. level) are welcome to apply. Research or development experience in machine learning or robotics (academic, internship, or personal projects are all acceptable).

Technical Skills

• Software development experience with Python and C++.
• Experience using ROS (Robot Operating System).
• Basic knowledge of Transformer-based models, Reinforcement Learning, or Imitation Learning.

Communication Skills

• Must be fluent in Japanese, at or above a business level. (Native level proficiency is strongly preferred.)
• Ability to clearly convey technical content through both oral and written communication in Japanese or English.
• Collaborative skills to work with team members and drive projects forward.

Preferred Qualifications

• Research and development experience in Physical AI, VLA, or Multimodal AI.
• Practical experience with simulation tools such as Isaac Sim, MuJoCo, Gazebo, or Omniverse.
• Proven control experience with physical robots (manipulators, mobile robots, humanoids, etc.).
• Knowledge of robotics vision technologies such as SLAM, 3D perception, force control, or visual servoing.
  • Implementation experience with Reinforcement Learning (RL), Imitation Learning (IL), or Diffusion Policy.
• Experience writing academic papers or presenting at international conferences.
  • Experience reading technical literature in English and collaborating with international teams.
• Experience developing deep learning models using PyTorch or TensorFlow.