RLWRLD is a leading Physical AI company developing a Robotics Foundation Model (RFM) that enables robots to perceive, reason, and act in the real world like humans.
Building on deep research capabilities in AI and robotics and a strong data collaboration network with industrial partners in Japan, Korea, and beyond, RLWRLD is rapidly advancing our RFM to enable precise manipulation by high-degree-of-freedom robotic hands. The company is also collaborating with world-class research groups and partners in robotics and sensor solutions to develop AI models that can be practically deployed across industries such as manufacturing, logistics, and services.
Having raised approximately KRW 60 billion in cumulative seed funding from leading domestic and global venture capital firms and major corporations, RLWRLD continues to attract exceptional talent who are eager to drive innovation across AI, robotics technology, and business.
About the Product Organization
At RLWRLD, our Product Organization is responsible for developing all core products — spanning planning, development, and research.
We are building foundational technologies such as:
- Robotics Foundation Model (RFM)
- APIs/SDKs to deliver RFM functionality
- Data pipeline & teleoperation tools
- Training systems for model learning
- Benchmark systems to test performance
- Robot control systems
- Infra stack (GPU orchestration, compute management)
Our team includes both research and software engineers, working fluidly across AI model development and software infrastructure. We collaborate closely with Academy Researchers, robotic hardware partners, and internal business developers to deliver cutting-edge robotics solutions.
Position Overview
We are seeking talented individuals who can work with human and object data to effectively transfer skilled human dexterity to robots.
In this role, you will be responsible for developing algorithms that estimate human motion and physical interactions using multimodal sensor data—including vision—and for designing data pipelines that enable these estimates to be leveraged for robot learning.
We are looking for engineers who are excited to tackle the challenge of converting human behavior into learning-ready data that robots can understand.
Key Responsibilities
- Human-Object Interaction (HOI) Analysis & 3D Pose Estimation
- Develop pipelines for accurate 3D hand/body pose estimation using multi-camera and egocentric-view setups
- Optimize shape and pose parameters using parametric models such as MANO and SMPL (e.g., beta/scale parameter estimation)
- Develop temporal and spatial calibration and synchronization algorithms across multiple sensors (cameras, IMUs, etc.)
- Human-to-Robot Motion Retargeting
- Develop retargeting algorithms that map human hand/arm trajectories to the kinematic constraints of robots (humanoids, robotic arms)
- Design and optimize grasp pose mappings that account for structural differences between human hands and robotic hands
- Validate physical feasibility of retargeted trajectories and apply post-processing (smoothing, interpolation, collision avoidance)
- Data Pipeline for Robot Learning (VLA)
- Build automated pipelines that convert large-scale human demonstration data into training datasets for Vision-Language-Action (VLA) models
- Apply data augmentation and inpainting techniques to reduce the sim-to-real gap
- Develop visualization tools and quantitative evaluation metrics for data quality validation
Required Qualifications
- Degree or equivalent practical experience in Computer Vision, Robotics, or a related field
- Strong understanding of 3D Vision & Geometry, including multi-view geometry, camera calibration, and 3D reconstruction
- Experience with parametric human models such as MANO, SMPL, FLAME, or other 3DMMs, including optimization
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Knowledge of motion processing, including 3D motion data handling, kinematics, and trajectory optimization
Preferred Qualifications
- Robotics Control & Simulation: Experience with robot kinematics, dynamics, and simulators such as Mujoco or Isaac Sim
- Deep Learning for 3D Humans: Hands-on experience implementing recent research in HMR (Human Mesh Recovery) or HOI detection
- System Integration: Experience with multi-camera systems, sensor fusion, and ROS/ROS2 development
- Large-Scale Data Processing: Experience building pipelines for large-scale video and 3D data processing
- Problem Solving: Strong perseverance and curiosity in tackling complex real-world challenges such as occlusion and sensor noise
Working Conditions
- Work Location: 561 Seolleung-ro, Gangnam-gu, Seoul (RUBINA Building, Yeoksam-dong)
- Employment Type: Full-time
- Probationary Period
- A three-month probationary period will apply upon employment.
- During this period, your work attitude and performance will be evaluated.
- Depending on the evaluation results, the probationary period may be extended or the employment offer may be withdrawn.
How to Apply
- Application Materials:
- Resume in English or Korean
- (optional) Portfolio, research materials, or project documents showcasing your capabilities
- Application Deadline: Rolling basis
Hiring Process
- Document Screening → 1st Interview → 2nd Interview → 3rd Interview → Final Offer
- Candidates who pass the document screening will be contacted individually.
- Additional Coffee Chats or Coding Test may be conducted if necessary.
Work Environment & Support
- Flexible Work Schedule: Adjust your working hours autonomously to match your personal rhythm.
- Equipment & Software Support: We provide job-specific equipment and essential software required for your role.
- Office Amenities: Enjoy our in-office snack bar and coffee machines.
- Holiday & Birthday Gifts: Small gifts are provided for holidays and birthdays.
- Health Checkup Support: We support your well-being through regular health checkups.
RLWRLD is a leading Physical AI company developing a Robotics Foundation Model (RFM) that enables robots to perceive, reason, and act in the real world like humans.
Building on deep research capabilities in AI and robotics and a strong data collaboration network with industrial partners in Japan, Korea, and beyond, RLWRLD is rapidly advancing our RFM to enable precise manipulation by high-degree-of-freedom robotic hands. The company is also collaborating with world-class research groups and partners in robotics and sensor solutions to develop AI models that can be practically deployed across industries such as manufacturing, logistics, and services.
Having raised approximately KRW 60 billion in cumulative seed funding from leading domestic and global venture capital firms and major corporations, RLWRLD continues to attract exceptional talent who are eager to drive innovation across AI, robotics technology, and business.
About the Product Organization
At RLWRLD, our Product Organization is responsible for developing all core products — spanning planning, development, and research.
We are building foundational technologies such as:
- Robotics Foundation Model (RFM)
- APIs/SDKs to deliver RFM functionality
- Data pipeline & teleoperation tools
- Training systems for model learning
- Benchmark systems to test performance
- Robot control systems
- Infra stack (GPU orchestration, compute management)
Our team includes both research and software engineers, working fluidly across AI model development and software infrastructure. We collaborate closely with Academy Researchers, robotic hardware partners, and internal business developers to deliver cutting-edge robotics solutions.
Position Overview
We are seeking talented individuals who can work with human and object data to effectively transfer skilled human dexterity to robots.
In this role, you will be responsible for developing algorithms that estimate human motion and physical interactions using multimodal sensor data—including vision—and for designing data pipelines that enable these estimates to be leveraged for robot learning.
We are looking for engineers who are excited to tackle the challenge of converting human behavior into learning-ready data that robots can understand.
Key Responsibilities
- Human-Object Interaction (HOI) Analysis & 3D Pose Estimation
- Develop pipelines for accurate 3D hand/body pose estimation using multi-camera and egocentric-view setups
- Optimize shape and pose parameters using parametric models such as MANO and SMPL (e.g., beta/scale parameter estimation)
- Develop temporal and spatial calibration and synchronization algorithms across multiple sensors (cameras, IMUs, etc.)
- Human-to-Robot Motion Retargeting
- Develop retargeting algorithms that map human hand/arm trajectories to the kinematic constraints of robots (humanoids, robotic arms)
- Design and optimize grasp pose mappings that account for structural differences between human hands and robotic hands
- Validate physical feasibility of retargeted trajectories and apply post-processing (smoothing, interpolation, collision avoidance)
- Data Pipeline for Robot Learning (VLA)
- Build automated pipelines that convert large-scale human demonstration data into training datasets for Vision-Language-Action (VLA) models
- Apply data augmentation and inpainting techniques to reduce the sim-to-real gap
- Develop visualization tools and quantitative evaluation metrics for data quality validation
Required Qualifications
- Degree or equivalent practical experience in Computer Vision, Robotics, or a related field
- Strong understanding of 3D Vision & Geometry, including multi-view geometry, camera calibration, and 3D reconstruction
- Experience with parametric human models such as MANO, SMPL, FLAME, or other 3DMMs, including optimization
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Knowledge of motion processing, including 3D motion data handling, kinematics, and trajectory optimization
Preferred Qualifications
- Robotics Control & Simulation: Experience with robot kinematics, dynamics, and simulators such as Mujoco or Isaac Sim
- Deep Learning for 3D Humans: Hands-on experience implementing recent research in HMR (Human Mesh Recovery) or HOI detection
- System Integration: Experience with multi-camera systems, sensor fusion, and ROS/ROS2 development
- Large-Scale Data Processing: Experience building pipelines for large-scale video and 3D data processing
- Problem Solving: Strong perseverance and curiosity in tackling complex real-world challenges such as occlusion and sensor noise
Working Conditions
- Work Location: 561 Seolleung-ro, Gangnam-gu, Seoul (RUBINA Building, Yeoksam-dong)
- Employment Type: Full-time
- Probationary Period
- A three-month probationary period will apply upon employment.
- During this period, your work attitude and performance will be evaluated.
- Depending on the evaluation results, the probationary period may be extended or the employment offer may be withdrawn.
How to Apply
- Application Materials:
- Resume in English or Korean
- (optional) Portfolio, research materials, or project documents showcasing your capabilities
- Application Deadline: Rolling basis
Hiring Process
- Document Screening → 1st Interview → 2nd Interview → 3rd Interview → Final Offer
- Candidates who pass the document screening will be contacted individually.
- Additional Coffee Chats or Coding Test may be conducted if necessary.
Work Environment & Support
- Flexible Work Schedule: Adjust your working hours autonomously to match your personal rhythm.
- Equipment & Software Support: We provide job-specific equipment and essential software required for your role.
- Office Amenities: Enjoy our in-office snack bar and coffee machines.
- Holiday & Birthday Gifts: Small gifts are provided for holidays and birthdays.
- Health Checkup Support: We support your well-being through regular health checkups.