Robotics Engineer - Embodied AI

YYCircle (SG) Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

YYCircle (SG) Pte Ltd seeks an Embodied AI Engineer to develop and deploy intelligent behaviors on humanoid robots. You will lead data collection, model training, and real‑robot validation, spanning perception, planning, and embodied decision‑making.

Responsibilities include testing failures, data‑driven improvements, and coordinating across AI, robotics, and hardware teams to deliver robust robot systems in real‑world environments.

Qualifications

• Good knowledge of VLA models, imitation learning, reinforcement learning, 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

Education

Master's degree or above in Robotics, Computer Science, AI, Engineering

Tools

Cloud-based GPU infrastructure

Job description

Embodied AI Engineer

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 startup-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