Robotics Engineer - Embodied AI

YY circle; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

YY circle seeks an hands-on Embodied AI Engineer to develop and deploy robot learning on physical humanoid robots. You will own data collection, model training, and validation from lab to real-robot environments, balancing research with practical deployment.

The role spans AI development, robot integration, data collection, testing, and iterative performance improvement across simulation and real-world scenarios.

Qualifications

• Master’s degree or higher in Robotics, CS, AI, or a related field.
• Willingness to work across AI, robotics, hardware, data collection, testing, and operations.
• Hands-on experience with robotic systems, including integration, calibration, or teleoperation.
• Good knowledge of VLA models, imitation learning, RL, or diffusion policies.

Responsibilities

• Develop, integrate, and fine-tune robot foundation models for perception, mapping, task planning, and embodied decision-making.
• Own the development cycle from data collection and model training to physical-robot deployment and validation.
• Test and analyze system failures, then improve performance through targeted data collection, model retraining, and iterative validation in both simulation and real-world environments.
• Track key performance metrics, including task success rate, completion time, intervention rate, repeatability, and safety.

Skills

VLA models

Imitation learning

Reinforcement learning

Diffusion policies

Robotic systems

Data collection

Education

Master’s degree or above

Tools

Cloud GPU infrastructure

Job description

About the Role

We are building in-house humanoid robotics capabilities (perceive, reason, and act autonomously)for real-world service and operational applications.

We are looking for a hands-on Embodied AI Engineer with experience in robot learning to develop, fine-tune, deploy, and validate intelligent behaviors on physical humanoid robots.

This is a start up-style role with broad ownership across AI development, data collection, robot integration, testing, failure analysis, and continuous performance improvement. The ideal candidate is comfortable working across disciplines and taking ideas from early experimentation through to real-robot deployment.

Key Responsibilities

• Develop, integrate, and fine-tune robot foundation models for perception, mapping, task planning, and embodied decision-making.
• Own the development cycle from data collection and model training to physical-robot deployment and validation.
• Test and analyze system failures, then improve performance through targeted data collection, model retraining, and iterative validation in both simulation and real-world environments.
• Track key performance metrics, including task success rate, completion time, intervention rate, repeatability, and safety.

Minimum Requirements

• Good knowledge of VLA models, imitation learning, reinforcement learning, or diffusion policies.
• Master’s degree or above in Robotics, Computer Science, AI, Engineering, or a related field.
• Willingness to work across AI, robotics, hardware, data collection, testing, and operations.
• Hands-on experience with robotic systems, including integration, calibration, or teleoperation.

Strongly Preferred

• Hands-on experience deploying and validating AI models on physical robots is a major advantage.
• Proven experience deploying learned policies on real robotic systems, including humanoid, bimanual, dexterous, or contact-rich manipulation.
• Experience across data collection, model training, deployment, and real-robot validation.
• Experience training and fine-tuning large robot-learning models using cloud-based GPU infrastructure.
• Demonstrable hands-on work through deployed systems, projects, publications, or robot demonstration videos.

What We Offer

• The opportunity to work directly on embodied AI models and deploy them on physical humanoid robots.
• A hands-on role combining research, model development, system integration, and real-world deployment.
• The chance to see your work move beyond simulation and operate in real environments.

Work Type

Full-time |On-site