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 believe that data is just as critical to AI performance as model architecture.
This role goes beyond simple data collection to define and execute a data-centric strategy that enables robots to operate effectively in real-world environments. The scope includes data design, curation, and validation, as well as measuring the value of training data and designing benchmarks. You will analyze and resolve model performance degradation from a data perspective.
We are looking for individuals who want to proactively push the performance limits of robotics AI through data-driven approaches.
Key Responsibilities
- Training Data Strategy Design
- Design data composition strategies tailored to robotics tasks (e.g., success/failure ratios, rare cases, contact-rich scenarios, edge case definitions).
- Establish quantitative criteria for data diversity, coverage, and difficulty distribution.
- Define and prioritize data types that most effectively contribute to model performance improvements.
- Training Data Execution and Quality Management
- Clean, structure, and manage multimodal robot data (vision, state, action, force/torque, etc.).
- Build pipelines to detect and filter data noise, missing values, and anomalous samples.
- Establish dataset versioning and reproducible dataset management systems.
- Data Pattern Analysis and Visualization
- Identify bias, coverage gaps, and redundancy through training data distribution analysis.
- Trace model failure cases back to data-level root causes and perform in-depth analysis.
- Share data and performance trends using dashboards and visualization tools.
- Benchmark Design and Development
- Design evaluation metrics and benchmark scenarios suitable for robotics tasks.
- Build evaluation frameworks to validate the impact of training data changes on model performance.
- Analyze correlations between offline, dataset-based evaluations and real-robot performance.
- Robot Performance Measurement and Insight Generation
- Evaluate robot policy performance both qualitatively and quantitatively, and derive data-driven improvement insights.
- Identify data-deficient regions based on failure logs, trajectories, and sensor data.
- Share insights with modeling and engineering teams to inform training strategies.
- Team Collaboration and Monitoring
- Collaborate with modeling teams and robotics engineering teams to define data requirements.
- Support evidence-based decision-making regarding the impact of data changes on model performance.
- Monitor and respond to data quality issues and operational risks.
Required Qualifications
- Data Format and Systems Knowledge
- Experience handling large-scale, complex data formats such as Parquet, MCAP, and protobuf.
- Understanding of multimodal time-series data structures.
- Data Analysis and Visualization Skills
- Strong problem diagnosis skills through data distribution and statistical analysis.
- Experience with visualization and monitoring tools such as Grafana, Kibana, or Superset.
- Software Engineering and Collaboration Skills
- Experience with Python-based data processing and analysis.
- Familiarity with development workflows including Git-based collaboration, code reviews, and test automation.
- Strong documentation and technical communication skills.
Preferred Qualifications
- Data Labeling and Automation Experience
- Experience building automated labeling pipelines using OpenCV, PyTorch, or TensorFlow.
- Hands-on experience with robotics data labeling, such as segmentation, keypoints, and trajectory annotation.
- Experience improving data efficiency through Active Learning or hard-example mining.
- Robotics / Machine Learning Domain Knowledge
- Experience working with robotics training data (teleoperation, simulation, real-world logs).
- Understanding of learning paradigms such as Reinforcement Learning (RL), Imitation Learning, and VLA.
- Experience explaining model failures from a data-centric perspective.
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 believe that data is just as critical to AI performance as model architecture.
This role goes beyond simple data collection to define and execute a data-centric strategy that enables robots to operate effectively in real-world environments. The scope includes data design, curation, and validation, as well as measuring the value of training data and designing benchmarks. You will analyze and resolve model performance degradation from a data perspective.
We are looking for individuals who want to proactively push the performance limits of robotics AI through data-driven approaches.
Key Responsibilities
- Training Data Strategy Design
- Design data composition strategies tailored to robotics tasks (e.g., success/failure ratios, rare cases, contact-rich scenarios, edge case definitions).
- Establish quantitative criteria for data diversity, coverage, and difficulty distribution.
- Define and prioritize data types that most effectively contribute to model performance improvements.
- Training Data Execution and Quality Management
- Clean, structure, and manage multimodal robot data (vision, state, action, force/torque, etc.).
- Build pipelines to detect and filter data noise, missing values, and anomalous samples.
- Establish dataset versioning and reproducible dataset management systems.
- Data Pattern Analysis and Visualization
- Identify bias, coverage gaps, and redundancy through training data distribution analysis.
- Trace model failure cases back to data-level root causes and perform in-depth analysis.
- Share data and performance trends using dashboards and visualization tools.
- Benchmark Design and Development
- Design evaluation metrics and benchmark scenarios suitable for robotics tasks.
- Build evaluation frameworks to validate the impact of training data changes on model performance.
- Analyze correlations between offline, dataset-based evaluations and real-robot performance.
- Robot Performance Measurement and Insight Generation
- Evaluate robot policy performance both qualitatively and quantitatively, and derive data-driven improvement insights.
- Identify data-deficient regions based on failure logs, trajectories, and sensor data.
- Share insights with modeling and engineering teams to inform training strategies.
- Team Collaboration and Monitoring
- Collaborate with modeling teams and robotics engineering teams to define data requirements.
- Support evidence-based decision-making regarding the impact of data changes on model performance.
- Monitor and respond to data quality issues and operational risks.
Required Qualifications
- Data Format and Systems Knowledge
- Experience handling large-scale, complex data formats such as Parquet, MCAP, and protobuf.
- Understanding of multimodal time-series data structures.
- Data Analysis and Visualization Skills
- Strong problem diagnosis skills through data distribution and statistical analysis.
- Experience with visualization and monitoring tools such as Grafana, Kibana, or Superset.
- Software Engineering and Collaboration Skills
- Experience with Python-based data processing and analysis.
- Familiarity with development workflows including Git-based collaboration, code reviews, and test automation.
- Strong documentation and technical communication skills.
Preferred Qualifications
- Data Labeling and Automation Experience
- Experience building automated labeling pipelines using OpenCV, PyTorch, or TensorFlow.
- Hands-on experience with robotics data labeling, such as segmentation, keypoints, and trajectory annotation.
- Experience improving data efficiency through Active Learning or hard-example mining.
- Robotics / Machine Learning Domain Knowledge
- Experience working with robotics training data (teleoperation, simulation, real-world logs).
- Understanding of learning paradigms such as Reinforcement Learning (RL), Imitation Learning, and VLA.
- Experience explaining model failures from a data-centric perspective.
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.